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Tag: artificial intelligence

  • Finding Value in AI-Augmented DevOps

    Finding Value in AI-Augmented DevOps

    While artificial intelligence (AI) and machine learning (ML) are emerging technologies, we know they can help an organization parse large data sets and glean actionable insights. But do AI-infused processes actually make a difference in organizations that employ DevOps? The answer is explored in a recent survey by Tricentis. Working in partnership with Techstrong Group, Tricentis surveyed more than 2,600 DevOps practitioners across the globe about their AI-augmented DevOps processes. The results pointed to a bright future in DevOps.

    In this survey, the majority of respondents cited they’re already getting value from AI-powered DevOps processes. A third of survey respondents found AI-augmented DevOps extremely useful today and nearly half found it very useful. Although the technology is still relatively young, a 2021 report by IT infrastructure automation company Puppet Labs found that 83% of organizations are implementing DevOps processes. This rapid adoption speaks to the promise of the technology.

    Interestingly, nearly two-thirds of respondents cited that testing is the area within DevOps where AI will have the most impact. Testing is a major pain point for DevOps organizations because a scaled DevOps environment calls for complex testing scenarios and large amounts of data, and many organizations struggle to scale their automation to the level DevOps requires. Without automation, manual testing can significantly delay releases. In AI-infused DevOps processes, AI can help accelerate the authoring of tests, help testers understand where the risks are, and repair broken tests.

    AI’s Greatest Potential is in Functional Testing

    We typically think of software testing as either functional, where the program enables users to properly execute tasks, or non-functional, where the application is secure, fast and stable. Respondents were asked which disciplines of testing stood to gain most from AI and 65% of them indicated functional testing. Unit testing and UI testing were the sub-categories most often selected.

    The complexity of the functional testing process is where AI shines. Functional testing is open-ended and many permutations are required to ensure proper test coverage. AI steps in and manages those permutations, parsing large amounts of data and providing insights. Additionally, AI can help wrangle production signals that aren’t discovered in a test environment.

    Throughout the survey, respondents specifically noted AI’s promise in regard to UI testing. UI testing is perhaps the most time-intensive and manual type of testing in most organizations. With the many variations of the user experience paths that exist, AI can help mimic the actions of a real end-user, adjusting for application nuances or variations in experience. AI and ML can also boost your build process, increasing the efficiency of repetitive tasks. The ability to quickly identify and resolve issues can improve the effectiveness of your automation.

    While survey respondents see the most potential in testing, they acknowledge that AI-powered DevOps can address other business challenges. More than 40% claim that these processes can reduce the skills gap and empower junior staff to perform complex tasks, improve the customer experience, cut costs, boost innovation and increase efficiency among developers. Nearly 40% also believe that AI-powered DevOps delivers leadership the insights they need for continuous improvement.

    Automation is the Key to Scaling DevOps

    It’s worth noting that among the DevOps organizations that see benefits from AI and ML, most of them are mature organizations; they employ DevOps workflow pipelines, toolchains, automation and cloud technologies, and they’ve automated more than half of their testing. Only 21% of practitioners who are just dipping their toes into DevOps have achieved this level of automation. And even in mature organizations, testing is still the main bottleneck; only 40% of them have automated more than half of their testing.

    The key to scaling DevOps practices is developing test automation that can keep up with an increasing volume and speed of releases. But the road to DevOps isn’t always easy. Practitioners cited obstacles like a lack of tech skills (44%), insufficient budget (25%) and tool selection (19%). Organizations that want to implement DevOps need to rely on external vendors and internal resources to address these challenges and effectively scale.

    Looking to Adapt More DevOps Practices?

    So where does an organization that wants to implement DevOps start? With the big questions. It’s crucial to identify the specific business problem that you’d like to solve. What’s the gain you’re looking for? Where in the software life cycle do you expect to find a benefit? AI and ML can add business value, but they don’t solve every problem across the life cycle.

    After an organization has identified its DevOps goals, its focus should shift to automation. To reap the benefits of AI-powered testing, an organization should automate repetitive functional testing, especially unit and regression testing. Most organizations start by implementing continuous integration and continuous delivery. Once an organization has taken these steps, it will start to amass a large cache of results and artifacts generated from running the test cases, which AI can mine to increase test stability and identify recurring problems. The more you automate, the more you’ll get out of it and the faster you’ll accelerate your delivery.

    Organizations just starting out with DevOps should also understand the distinction between artificial intelligence and ML. Most people refer to ML as AI, but they’re not interchangeable, and most organizations haven’t advanced to the use of AI. A true AI is broadly powerful, while ML technologies are narrow and solve a specific domain problem that a model was trained for.

    When you’re evaluating potential vendors, there are a number of key questions to ask, including:

    • What specific AI technology is the vendor using? Is it an off-the-shelf library or something they built themselves?

    • How are they using AI? Which algorithms are in play and what approach do they take?

    • How do they train their models? Different types of patterns emerge in data sets depending on the industry, the frequency with which data is updated and the application. The data population that the vendor is training on must look like your production data, or the model could take you in the wrong direction.

    • How do they ensure that bias doesn’t come into play? If they train models in a different domain or vertical, for example, you could be an outlier for that vendor.

    • How do they handle failure? How do they monitor for miscategorization and misapplication of the AI in a way that lets you trust the data?

    • Will your data be used to train and improve your learning models? If so, does that raise any privacy concerns? Expectations around data privacy should be explicit and leave no questions.

    • Are there other customers in your vertical with a similar business profile that are getting the benefits you’re looking for? Can the vendor provide examples?

    What Does AI-Infused DevOps Look Like in Practice?

    Consider a financial services organization that has more than 20 applications to manage. With so many applications, it’s necessary to conduct significant UI testing and the tests must be stable. One of the biggest challenges to UI testing lies in locating various web elements; most people start with manual script-based automation, but it can be challenging and time-consuming to create and maintain. Employing AI and smart locators to identify these elements can save time and drastically cut down on UI testing maintenance.

    Survey respondents were asked how they’re currently using AI to augment their testing processes. Thirty-seven percent say they’re accelerating the creation of their automated test cases, while 44% focus their testing on the areas of highest risk. Almost half are able to reduce test case maintenance with self-healing and 43% use AI to identify the root cause of failed tests. Thirty-one percent claim that AI offers insights into test process improvements and 34% use AI to help identify tests to run based on changes within the application.

    Where is AI-Powered DevOps Going Next?

    Going forward, AI will become more and more embedded in testing processes. Great progress with off-the-shelf vendors is being made, and their products are a lot more accessible than they used to be. As the tooling continues to mature, these products will reduce the need for specialized skills. Internal initiatives like data lakes or data warehouses will persist, but there will be less need to build large data science teams to get the advantages of AI.

  • MLOps Vs. DevOps: What’s the Difference?

    MLOps Vs. DevOps: What’s the Difference?

    Machine learning operations, or MLOps for short, is a key aspect of machine learning (ML) engineering that focuses on simplifying and accelerating the process of delivering ML models to production and maintaining and monitoring them. MLOps involves collaboration between different teams including data scientists, DevOps engineers, IT specialists and others.

    MLOps can help organizations create and improve the quality of their AI and machine learning solutions. Adopting MLOps allows machine learning engineers and data scientists to collaborate to improve model performance by implementing continuous integration and continuous deployment (CI/CD) practices. It accelerates the ML model development process by incorporating the appropriate monitoring, governance, and validation of ML models.

    What Is DevOps?

    DevOps combines the concepts of development and operations to describe a collaborative approach to performing the tasks usually associated with separate application development and IT operations teams. DevOps, in its broadest sense, is a philosophy that encourages improved communication and cooperation between these (and other) teams within an organization. 

    In its narrowest sense, DevOps refers to adopting practices that enable the deployment and maintenance of iterative application development, automation, and programmable infrastructure. It also includes changes in the workplace culture, like trust-building and bonding between developers, system administrators and other team members. DevOps aligns technology with business objectives and can transform the software delivery chain, job functions, services, tools and best practices.

    MLOps Vs. DevOps: Key Differences

    Here are some of the main differences between MLOps and traditional DevOps.

    Development

    The concept of development refers to different things in each model, with a slightly different CI/CD pipeline.

    DevOps:

    • Usually, the code creates an interface or application. 
    • The code is wrapped into an executable or artifact before being deployed and tested with a set of checks. 
    • Ideally, this automated cycle will continue until the final product is ready.

    MLOps:

    • The code enables the team to build or train machine learning models. 
    • The output artifacts include serialized files that can receive data inputs to generate inferences. 
    • Validation involves checking the trained model’s performance based on the test data.
    • This cycle should also continue until the model reaches a specified performance threshold.

    Version Control

    DevOps:

    • Version control typically only tracks changes to code and artifacts.
    • There are few metrics to track.

    MLOps: 

    • MLOps pipelines usually have more factors to track. Building and training an ML model involves an iterative experimentation cycle, requiring tracking of various metrics and components for each experiment (essential for later audits). 
    • Additional components to track include training datasets, model building code and model artifacts. 
    • Metrics include hyperparameters and model performance indicators, such as error rates.

    Reusability

    DevOps:

    • DevOps pipelines focus on repeatable processes.
    • Teams can mix and match processes without following a specific workflow.

    MLOps: 

    • MLOps pipelines repeatedly apply the same workflows. The common framework across projects helps improve consistency and allows teams to progress faster because they start with familiar processes. 
    • Project templates offer structure, enabling customization to address the unique requirements of each use case.
    • Uses centralized data management to consolidate the organization’s data to accelerate the discovery and training processes. Common approaches to centralization include a single source of truth and data warehouses.  

    Continuous Monitoring

    Monitoring is essential for both DevOps and MLOps, but for slightly different reasons.

    DevOps:

    • Site reliability engineering (SRE) has been trending over the past few years, emphasizing the need for monitoring software from development through to production deployment. 
    • The software does not degrade in the way an ML model does. 

    MLOps:

    • Machine learning models can degrade quickly, requiring constant monitoring and updating.
    • Conditions in the production environment affect the model’s accuracy. After deployment to production, the model starts generating predictions based on new data from the real world. This data is constantly changing and adapting, reducing model performance. 
    • MLOps ensures that algorithms remain production-ready by incorporating procedures to facilitate continuous monitoring and model retraining.

    Infrastructure

    DevOps and MLOps both rely heavily on cloud technology, but have different operational requirements.

    DevOps relies on infrastructure such as:

    • Infrastructure-as-code (IaC)
    • Build servers
    • CI/CD automation tools

    MLOps relies on infrastructure such as:

    • Deep learning and machine learning frameworks
    • Cloud storage for large datasets
    • GPUs for deep learning and computationally-intensive ML models

    DevOps and MLOps Trends

    Here are some of the major trends driving the development of DevOps and MLOps.

    GitOps

    A new evolution of the DevOps workflow, GitOps is a new paradigm for controlling and automating infrastructure. A Kubernetes-oriented paradigm enables developers and operations teams to manage Kubernetes clusters and deliver containerized applications using Git. Implementing Git workflows for operations and development teams allows developers to leverage Git pull requests to manage software deployments and infrastructure.

    GitOps incorporates existing development tools to manage cloud-native and cluster-based applications with CI/CD. It automatically deploys, monitors, and maintains cloud-native applications using a Git repository as the single source of truth.

    GitOps is a way to implement and maintain clusters in Kubernetes. Continuous delivery and deployment allow developers to build, test, and deploy software faster through incremental releases. Kubernetes continuous integration and runtime pipelines must be able to read and write files, update container repositories, and load containers from Git. GitOps helps businesses manage their infrastructure with version control, real-time monitoring, and alerting of configuration changes.

    Synthetic Data

    Synthetic data is any information generated artificially instead of collected from real events. Algorithms generate synthetic data for use as a replacement for operational and production test datasets. Synthetic datasets are also useful for validating mathematical models and training machine learning models.

    Benefits of synthetic data include:

    • Minimizing the constraints associated with using sensitive and regulated data.
    • Customizing data to specific requirements and conditions not available in real-world data.
    • Generating data for testing software quality and performance for DevOps teams.

    Codeless Machine Learning and AI

    Machine learning often involves computer code to set up and process model training, but this is not always the case. Codeless machine learning is a programming approach that eliminates the need for ML applications to go through time-consuming processes.

    Codeless ML eliminates the need for experts to develop system software. It is also simpler and cheaper to deploy and implement. Using drag-and-drop input during machine learning processes can simplify training efforts in the following ways:

    • Evaluating results.
    • Dragging and dropping training data.
    • Creating predictive reports.
    • Using plain-text queries.

    Codeless ML gives developers easy access to machine learning applications, but it is no substitute for an advanced, nuanced project. This approach is suitable for small businesses that lack the capital to maintain an in-house data science team.

    TinyML 

    TinyML is a new approach to machine learning and AI model development. It involves running models on devices with hardware restraints, such as microcontrollers powering smart vehicles, refrigerators and electric meters. This strategy is the best fit for these use cases because it speeds up the algorithm—there is no need for the data to go back and forth to a server. It is especially important on large servers and can speed up the entire ML development process.

    Running TinyML programs on an IoT edge device has many benefits:

    • Lower energy consumption.
    • Reduced latency.
    • User privacy guarantees.
    • Reduced bandwidth requirements.

    Using TinyML offers greater privacy because the computation process is entirely local. It consumes less power and bandwidth, resulting in lower latency because it doesn’t require sending data to a central location for processing. Industries that are taking advantage of this innovation include agriculture and healthcare. They typically use IoT devices embedded with a TinyML algorithm to monitor and predict real-world events using collected data.

    Conclusion

    In this article I covered the key differences between MLOps and DevOps:

    • Development—DevOps pipelines focus on developing a new version of a software product, while MLOps focuses on delivering a working machine learning model.
    • Version control—DevOps is mainly concerned with tracking binaries and software artifacts, while MLOps tracks additional factors like hyperparameters and model performance.
    • Reusability—DevOps and MLOps both strive to create reusable processes and pipelines, but use different strategies to achieve repeatability. 
    • Continuous monitoring—Monitoring is important to DevOps, but even more important in MLOps because models degrade in performance due to model and data drift.

    Finally, I covered a few key trends that will transform DevOps and MLOps in the near future. I hope this will be useful as you discover your place in the new and exciting development ecosystem.

  • DevOps Unbound EP 19 – How AI and ML are being used in DevOps Today – TechStrong TV

    DevOps Unbound EP 19 – How AI and ML are being used in DevOps Today – TechStrong TV

    Alan Shimel: Hey, everyone. I’m Alan Shimel, CEO of MediaOps, devopsy.kinsta.cloud, Container Journal, Security Boulevard, and you’re watching DevOps Unbound. DevOps Unbound is sponsored by our friends at Tricentis. So many thanks to them. And DevOps Unbound is a biweekly show where we cover topics of interest to the DevOps audience. I am the host. My co-host is our CTO and CEO of accelerated strategies group, my friend Mitchell Ashley. Mitchell, welcome.

    Mitchell Ashley: Thank you. Good to be here as always, and with this illustrious panel.

    Alan Shimel: Absolutely. And an illustrious panel, it is. We have two panel members joining Mitchell and I today, let me introduce you to both of them. They’re both great folks in their own and I’m gonna let them introduce themselves. Let’s start with our friend Judith Horowitz. Judith, welcome to DevOps Unbound.

    Judith Hurwitz: Thank you so much, Alan. It’s a pleasure and honor to be here.

    Alan Shimel: It’sour pleasure.

    Judith Hurwitz: So I’m Judith Hurwitz. I’m the co-author of 10 books. Have been in the industry for 30-plus years. Focus on everything from DevOps, security, manageability in cloud, hybrid cloud, and really looking at how you take technology to transform organizations. It’s a complex topic, not easy, but it’s what we’re in the middle of.

    Alan Shimel: Absolutely. Then last, but certainly not least, my friend Brian Dawson. Hey Brian, welcome.

    Brian Dawson: Hey, Alan. Thank you. Good to be on with you again. To tell the audience a bit about myself. As I’ve told you before, Alan, I’ve been in software development and delivery for about 30 years. I consider myself a technologist and even prior to focusing on DevOps, I’ve had a focus on optimizing software development and delivery.

    Excited to talk about AI. I kind of dabbled and dipped my toes in the space and my time that I spent at the company that is now PlayStation. Things have come a long way since, and during that time, I’ve spent the past 10 years of my career focused on identifying, applying and spreading DevOps practices. So I’m excited to discuss the two together here with this group.

    Alan Shimel: Absolutely.

    Participant: Brian, I’m not sure if you mentioned your present position with the Linux Foundation.

    Brian Dawson: I did not actually, because also this organization I’m at today has a big footprint in the AI and ML space. So today I’m with the Linux Foundation, I oversee our developer relations in ecosystem development. And of note within that is the LFAI & Data Foundation which houses a number of impactful products in this space projects. Excuse me.

    Participant: Thank you. I just thought the folks at LF would welcome that.

    [Crosstalk].

    Brian Dawson: Well, appreciate it. Thank you.

    Participant: Yep

    Alan Shimel: All right. So the topic for today’s DevOps Unbound is artificial intelligence and machine learning, how can they help improve performance of DevOps teams? In other words, what’s their role in the DevOps world? And let me preface our conversation by giving you sort of my view of it.

    DevOps for many people was all about automation. What can we automate? Let’s automate everything we can, automate, automate, automate. And obviously in discussions around automation, topics such as artificial intelligence and machine learning, you would tend to think help with automation. And therefore anything that helps with automation is good for DevOps.

    And I should also say that we lump AI and ML together, like their siamese twins joined at the hip, and they’re not necessarily conjoined. You can have AI without ML and you can have ML without AI, at least I think so. I’d be interested in your thoughts.

    But has it lived up to the hype? Will it ever live up to the hype? Was the hype unfounded? Was it unrealistic? What role has AI and/or ML to this point played on DevOps and what role will it play going forward? I think that’s our topic today. I mean, any one of you, if you want to kick it off with your own feelings or respond, go right ahead.

    Brian Dawson: Well, I’d like to jump in as a troublemaker early on and say –

    Alan Shimel: Okay.

    Brian Dawson: – I challenge Alan that DevOps is really about automation is kind of the basis for the tie. If we look at it, DevOps is really a set of cultural practices and tenants that align DevOps and other software delivery stakeholders around the shared objective of delivering quality software reliably, rapidly, and repeatedly.

    Now the reliably, rapidly and repeatedly component, absolutely lends itself to some of the benefits that we could mine from AI. But I actually think it’s a great opportunity if we don’t say DevOps equals automation equals AI, but rather we say DevOps equals a culture aligned around a shared objective and how can AI and ML help support those shared objectives.

    Judith Hurwitz: So I think you make a great point. Brian, I think one of the most important issues, and you mentioned culture. We have been, for what, 50 years through this development and operations perspective on how we get things done. And the promise of AI is, “Okay, can push a button and it will take care of everything.” And, and this promise has been there for, what, 20 years.

    The reality is, it’s just not that simple. There are definitely things that we are doing today, and we are seeing evolve that is definitely helping the developers and the operational professionals. For example, if you have repeatable functions that happen all the time, that for example, if you press a button and a certain task should happen, you can probably use a model and collecting massive amounts of data to do that automatically.

    nd that’s a very good use of AI, and will work today. They call it ML Ops or AI Ops, and there’s a good reason why that’s what we’re focused on today, is because you’re look for predictable patterns and predictable anomalies so you can avoid making stupid mistakes that, “Oh my God, why did I do that? I knew what the right answer was.”

    But AI is not a panacea. And I think if we look back even two, three years ago, you had companies that all were saying, “Okay, we have automated, we’ve put AI into DevOps and all you have to do is press button and all of your problems are over.” That’s just not reality.

    Alan Shimel: Yeah.

    Mitchell Ashley: Yeah, there’s the marketing AI and ML, and then there’s real AI and ML. Marketing, meaning everybody glams onto the term and uses it, meaning it’s a case statement or an if statement in people’s logic in their software. I started doing some work in AI in the 80s and 90s programming and Lisp prologue, and doing some corporate education in that area, some expert systems with triage, things like that.

    So dabbled in a little bit there. And then I was exposed to more recently, about five, six years ago, worked with a gentleman named Dr. Bernardo Huberman, who’s one of the experts in the field. And I asked him a me understand why is machine learning taken off as this sort of, he described it as a subset of AI. So you can know that was his model anyway.

    What he told me really made sense, and it kind of helps me understand where can we apply machine learning? And that being kind of the most popular part of AI. And he said, “Machine learning takes massive amounts of data.” And the fact that we have the cloud and all these applications, creating data, a lot of data exhaust. Now, people are going back and mining that data.

    So we have this whole, Judith, in your writing, in your books about data analysis and leading up to AI, machine learning is great for that, because you can have supervised or unsupervised algorithm.Supervised, meaning that’s a cat, that’s a cat, that’s not a cat, or unsupervised, which is gonna pour through the data and start to look for those patterns and trends and anomalies that you were talking about Judith.

    So when people talk about using AI or ML in our industry, I always think about, well, where’s there lots of data, and can we leverage it in some impactful way? So as I look at products or technologies that claim they’re doing that, that’s at least one sort of criteria to fair it out. Is it real there? Is there something real there, or is it more spin and fluff to help us? We all have case statements in our spot, right?

    Alan Shimel: Absolutely. Actually, Brian, it looked like you were gonna say something, I didn’t wanna jump on you.

    Brian Dawson: No, Alan. I mean, of course I have a lot to say, but nothing in particular, I just, since I’m off mute, I’ll say I absolutely agree with Mitch and Judith and underscore the points that they’ve made.

    Alan Shimel: Yep. You know what, I wanna take a moment though, and explore, Brian, you disagree DevOps isn’t about automation. And I get the whole cultural aspects of DevOps and all of that, but certainly automating as a way to be more efficient, to get more done faster, I think is part and parcel of the DevOps mindset.

    Brian Dawson: Yeah. And if I may and without waiting to see where you wanted to go with that, I’d say, no, it, it is absolutely important, but there’s a line Jez Humble would use in talking about CD. And frankly, I think the book that Dave Farley, Jez Humble and team, Road Around Continuous Delivery is undercelebrated and under referenced.

    And in talking about that, one of the things he said is continuous delivery. And I’m gonna say by extension, some aspects of DevOps don’t require any tools. I can do continuous delivery with a bash script.Catch is it’s not the most efficient in pursuit of effective collaboration of delivery velocity so you can iterate. And as we’ve talked about in this past, establish that control feedback loop that requires things like automation to achieve those goals.

    I just think sometimes when we talk about what we see going on with AI and ML, and in those terms, we overload a principle or practice with expectations that are core to it. And I worry a bit about that, so I wanna call it out. And sorry, Alan, I don’t know if that’s where you wanted to go with the commentary.

    Alan Shimel: No, no, no. I wanna go wherever you wanna go, Brian. It’s a collective. Judith, what about – I’m sorry, go ahead. Oh

    Judith Hurwitz: Oh, yeah. So,Alan, I think one of the key issues is how the whole area of continuous delivery. In previous generations, you would build an application and it would live pretty much as it was written, could be for 10 years. Today, applications are constantly having to be revised because customers change, partners change, the sources of data change.

    So, unless you have the ability to constantly update and change and modify, you lose out. One of the values of using automation and using AI and machine learning models with lots of data, is to support the ability to do this. You don’t have enough hands and enough brains to where problems may occur, because you’ve changed things.

    How many times have we seen problems occur because somebody has added a new service to their environment and somebody forgot to change a configuration file? Now that’s not something that you need a massive brain to be able to do, but people get busy and they forget to do simple things.

    Mitchell Ashley: I think, Judith, that’s really a good point, ’cause I wanted to ask you about this in the software creation process. Given things are so dynamic now and constantly changing, understanding just the environment, the infrastructure’s code all the way up through the application and how much all of that is changing, it seems like the stepping in for humans in certain conditions in that environment is a great application of AI.

    For someone to synthesize all the factors that might go into where a problem exists or what might be causing a problem, seems to be where some of those algorithms might be helpful. Do you agree with that? Has that been your experience of what people are thinking about for AI?

    Judith Hurwitz: Yeah, definitely. I think what we’re seeing is this is where sort of the human factor comes in. You set it up so that if the printers turned off, don’t send me an alarm fast emergency. There’s a problem with the printer. We all have gotten used to that. But when there are problems that you you’ve never seen before that you don’t have data on and the model doesn’t take into account, then the AI Ops or ML Ops then gives you a message, “There’s something strange going on. I don’t know how to fix it. What do you want do here?”

    And so over time, when that appears again, well now you had data, well, this occurred once before, maybe it was anomaly twice. But then when you have enough data and enough experience over time, then you build that into the model so the next time that occurs, you make a fix.

    On the other hand, you don’t want the system to say, “Oh, I know what that is, make a fix,” when it turns out, no, no, no, you don’t understand the context of, of what’s going on here. And just because there’s a correlation, doesn’t mean that there’s a cause for that.

    Mitchell Ashley: It’s good to know, ’cause it’s not always about automating a response to it. I know in the security world, we’re very cautious, we’re skeptical about those things happening, blocking, legitimate traffic and financial world. That’s a huge issue that we run into.

    So you pointed out a good scenario, whereas we see repetitive patterns over time and that can help machine learning algorithms now understand, “Okay, that’s what that pattern looks like,” so you can identify what it is next time instead of it’s just an anomaly.

    Brian Dawson: Yeah. It’s interesting Mitch that you say that, and going back to the original, kind of the starting topic about AI and ML, not really achieving, its its promise yet. And as you called out, getting data is an issue, it’s not necessarily applicable to every space. You need a level of determinism and predictable patterns that you can learn from and build on.

    And I have for a long time been excited that when we look at CI and CD, when we look at, Alan, the automated workflow of software development and delivery, or even if it’s not automated kind of the standard, there’s a couple of things that you do have. You’re doing builds, sometimes thousands of times a day, builds in deployment, getting that out into pro. You could be generating a ton of data.

    It’s also in its nature, you’re striving to achieve a level of consistency and repeatability in how you deliver software. And I’ll say I’m not an expert to the level. Some of the people on this episode are, but I get at a very sort of top level get really excited about the opportunity for AI and ML to help better CI and CD, and in line with the principles of DevOps.

    What I see it really helping with is reducing cognitive loads so developers can focus on coding, innovating and solving problems while helping ensure that quality and stability that’s difficult to maintain while you’re moving fast. So just thought I’d called out.

    And I am curious to see when vendors and others really start to do like what our friend Koski has done with Launchable, and really apply AI and ML to the left hand side or dev side of the process to reduce the load, insure quality and stability

    Alan Shimel: Guys, we’re all lumping AI and ML, AI and ML, AI, and ML. Does it have to be AI and ML? Isn’t it, in my experience, a lot of what we call AI and ML is a lot more on the ML than it is on the AI? So, is it fair to call it truly AI or is it really ML? And maybe ML is for today and AI is it tomorrow. I don’t know.

    Judith Hurwitz: So I think there’s definitely a problem of nomenclature here. What we’re really dealing with primarily now is models and modeling data and creating models from data. That’s the reality of today. I think that there is a lot of misuse of the term AI and there have been some absolutely wild predictions. I can’t remember the name of the computer scientist who predicted that he would be able to replicate the human brain with AI.

    Mitchell Ashley: Marvin Minsky treat.

    Judith Hurwitz: No, this was past Minsky.

    Mitchell Ashley: Oh, was it after? Okay.

    Judith Hurwitz: Yeah, no, I mean, this was in the last five years.

    Mitchell Ashley: Oh, okay.

    Judith Hurwitz: So, over time, you’re always gonna be dealing with models. You’re never gonna get rid of the models. AI is sort of the next – Models are a subset of what you eventually achieve with AI, but AI is really a concept that will probably take decades to evolve. And I think one of the problems we have right now is where you have vendors, and I’ve talked to hundreds of them over the years where they say, “We have an AI application.”

    It’s because it’s a hot buzzword and you can look back and in the history over the last 30 to 40 years and see whatever the hot topic is, all of the vendors say we do that. So I think that that’s one of the problems that businesses are facing right now. What is the difference? What does it really do? How does it make your company better? How do you use this technology to be prepared for change?

    Mitchell Ashley: I think that’s a good way to break it down too, because early days of AI were about emulating human thinking. We’re still long from – I’m not sure – All my intelligence is artificial, by the way, I acquired all of it. I don’t think I was born with any of it. But that was kinda where it started. And then AI became about expert systems largely, and then machine algorithms.

    Machine learning algorithms was really where I think most of the activity is today because of that prevalence of data. It seems like most organizations are faced with sort of one of three strategies, what do we look from our vendors and how they may use AI in a meaningful way, or machine learning in a meaningful way that’s gonna help my business.

    Do I build models myself from the data like you were talking about, Judith, to apply to like a financial analysis kind of situation, more of an expert system, or do I use machine learning algorithms in my own software to do interesting, valuable things that my software can do.

    And maybe you play in all three of those or a subset of those, but that seems to be the question I think most individuals or organizations are at. We’re not at a place where we can go build models, but we’re looking for these capabilities either in our own code or in third party products.

    Judith Hurwitz: So I heard an interesting story from one of my clients a few years ago, maybe five years ago. He had a client that was very gungho about AI. So he went out and he hired five data scientists, paid them a million dollars each, gave them their own space and left them alone. These are the smartest people on the planet. Came back in six months. Didn’t wanna bother them. They’re so smart.

    “All right. What have you found out? Have you written this application?” And they said, “Well, we’ve been discussing this for six months and we have determined what algorithm we’re going to use. The point is, that they worked in isolation. They thought they were the smartest people on the planet. They did not talk to people on the business side about what business problems they had.

    They didn’t talk to the people who understand corporate data. They didn’t ask them, “What data do you actually have? What data do you need?” They didn’t talk to people who knew the business strategy or the business processes that were in place or needed to change. So it really is a team sport.

    And I like Brian’s discussion in the beginning about culture, because it really is about sort of hybrid organizations where you have people, you have leaders that know a little bit about all of these areas, and then a team that’s brought together that can work across these areas.

    Mitchell Ashley: Sure.

    Brian Dawson: Judah, can I ask, do you have any recommendations for DevOps teams on how do we evaluate or investigate where ML is a solution? And I ask this based on observation, we’ll see if we agree, that oftentimes people are looking for a problem to apply the solution to, as opposed to looking for ML as a solution to a problem they’ve identified. I’m just curious if you [crosstalk].

    Judith Hurwitz: I violently agree with you.

    Brian Dawson: Okay.

    Judith Hurwitz: So I think, and a lot of times people get so enamored with a new technology that they look at it as a way to solve all problems.And for a DevOps team, I think we are finally getting to the stage where there’s really reality in DevOps. It’s not the developers who are saying, “That’s not my problems. The operations people have to make this work.”

    There’s really beginning to be this collaboration between development and operations. And this shift left is definitely becoming real. So that’s definitely true, but for these teams to be successful, they have to have a holistic view of, where is your business going? What do they actually need? Why have they come to you and say, “We gotta do something?”

    Is it just because it would be cool to do something and spend money,or is there a real rationale behind this that they need from? What’s the pain that’s out there that they can solve? So, they have to start with that. They have to start not only collaborating between developers and the operations team, but with the business leaders, with the people who understand all of the data, people who understand security. So, all of this comes together in very much a holistic pattern.

    Alan Shimel: Fair, fair. Hey guys, we’re way past halfway through here and I wanted to kind of turn our conversation to the future. We’ve had a discussion on sort of the history of AI and ML and lessons learned, et cetera. But when we look forward, Brian, you mentioned the Linux Foundation has a, I dunno if it’s a daughter foundation or a subgroup, dedicated to AI and technologies.

    What do we see when we look sort of near term future? Forget, yes, one day, we’ll mimic human brain patterns, who knows if we’ll be alive by that, but near term, what do you see? What is Linux Foundation planning for?

    Brian Dawson: Well, looking forward, I – And it’s funny, ’cause you brought me in and I started to think about the impossible dream of what’s gonna happen in the future. I will call out and will mention again, I said at the start, the Linux Foundation is a parent foundation of what we call a Linux Foundation open source project that hosts other project.

    So LFAI & Data, you can call it a sub-foundation of Linux Foundation because they host, I believe at this point, 12 graduated projects with about 20 to 30 projects in incubation. So we’re talking 30 to 50 projects under the LFAI & Data umbrella that are all working on various aspects of shared efforts, multiple, large commercial organizations, as well as standards bodies coming together to drive the future of of AI and ML.

    Brian Dawson: What I do see coming out of LF AI & Data Foundation in the short-term is standards and foundational implementations, i.e. moving sort of beyond the sort of rudimentary discovery around AI, building sort of packaged or gray box implementations that everybody agrees and collaborates on, which I think will help unlock the less initiated, less expert vendors to begin to deliver truly AI based differentiating capabilities.

    So, to put that in short, because there was a lot of words is, I think it’s about establishing a foundation for us to start to build on and accelerate our progress within the AI and data space applied to DevOps.

    Judith Hurwitz: And so, Brian, I think that you are 100 percent right. And that’s when commercialization really happens, is when we have those standards and when everybody agrees to use those foundational services. I think the challenges that we face are, how to get the commercial vendors who don’t necessarily want to –

    They want to give lip service to these standards, but they really want you to only use their version of the “standard” so that customers never leave. So, it’s that stickiness factor that they are looking for. So, I think it’s it’s a hard journey.

    Brian Dawson: Yeah. Well, I think, Judith, you actually nailed the reason the Linux Foundation exists, frankly. And if we look at Kubernetes as a model, it was hard. Press Google could have easily said, “We’re not going to hand this over to the Linux Foundation. We’re gonna dominate this space. We are going to be the modern cloud OS.”

    But they understood for it to gain traction, for it to grow and truly offer benefit industrywide, they had to hand it over to the Linux foundation, a grow manage. They had to bring in Microsoft. They had to bring in Amazon to play in that space. And I see LFAI & Data serving that same role, what I tend to call in some language unlocking innovation, or as our tagline is, decentralized innovation.

    And I would beg to say that that is, we didn’t call that out directly, but one of the challenges that we’re seeing in the AI and data space. If there’s not short-term monetization, we’re not gonna do it. And if there is, then we wanna own it. So can we create a impartial playing field for everybody to come in and innovate together, and then build commercial solutions off of that?

    Judith Hurwitz: Yeah, it’s the challenge. It really is. And I think Kubernetes is a great example. I think data in some ways is more complicated because data’s really the crunch –

    Brian Dawson: Yeah. Well, I know everybody wants to own it. Everybody wants to own the data.

    Alan Shimel: No doubt about it.

    Mitchell Ashley: There’s discussion about AI needs to become an engineering discipline as opposed to this sort of edge specialty. Doesn’t mean that necessarily is gonna be applying everywhere, but I think how we apply DevOps or, I’m sorry, AI to certain kinds of problems, it seems to me the two most ripe areas are that highly complex environments.

    So it’s more and more infrastructure, it’s automated, more things are gone to digital. How do you manage the infrastructure or manage the triage problem-solving? And then of course, I think that the other areas, people will continue to find niche areas where AI can be applied to gain competitive advantage in a certain domain or space.

    And it seems like that’s the trajectory we’re on for quite a while. I don’t think it’s ever gonna be AI takes over everything, and it’s the thing that replaces DevOps or whatever. But it seems to me kinda a tool, but – Sorry, go ahead, Judith.

    Judith Hurwitz: No, what I was gonna say is very interesting. If you look at healthcare, for example, if you look at the spectrum firm, I can automate certain DevOps functions that are repeatable, where I can identify patterns. And as I collect more data, I can automate more things.

    You have something like healthcare, which in terms of orders of magnitude of complexity are just huge. And what I’m seeing is a lot of the vendors who think that I’m going to tackle healthcare with AI and we’re gonna own the industry, a lot of them are getting out of the business because right now, it’s just too hard and it will be too hard for quite a while.

    Mitchell Ashley: It’s true of the medical industry in many ways. It’s a hard nut to crack.

    Brian Dawson: Yeah. If I may start to dream a little and chime in based on something you said Mitch, I do see that in the near term, we are at a point where we talk about modern software development and delivery, the rapid pace which we’re delivering change, we’re building on inventions and progress made over decades. Library reuse is really heavy.

    And we are getting to a point where as we build more complex systems, we have to figure out how can we outsource sort of maintenance and management for one of a better word to use it kind of grossly to solutions like ML? How can ML help us continue to improve, grow and build on what we’ve done, but manage the scale and complexity?

    And I think we’ll move from some standardization, foundational blocks will apply more ML and both the operations and development space to manage that complexity, to maintain stability. But then I also eventually see the next stage being now, how do we apply ML and surface it to a developer at the time of a commit?

    Here’s what the expected outcome of this changes to help provide guardrails to the developer, the end state, or I wouldn’t even say the end state. I think we’d agree existentially there’s never an end state here. That we’re at a point where we don’t even have explicit CICD pipelines. We can commit code to a repository, the language can be inferred.

    We can give queues or signals to infer where we want it deployed, how we want it deployed. And I actually see ML and to an extent, layers of AI, just figuring out what to do with code at rest. So if you flash forward in 10 years, and we’re truly applying these to technologies at scale in the cloud, I just change a line of code.

    That change automatically is delivered to production. And AI/ML is helping us do that. So that’s sort of my dream. I don’t hit compile, I don’t hit build, I don’t build out stages and workflows, I just change code, and that’s running in a system somewhere.

    Judith Hurwitz: So, don’t you also want to have the ability to say before you say, you look at what’s happening, and don’t you wanna sort of at least at this stage say, “Are you sure you wanna do that? Are you sure you wanna delete that file?”

    So until we get to the point, because you have to be able to trust that the system is smart enough to understand if I make this change, what cause will that unleash? Because we’re not dealing with the perfect world in DevOps by a long shot.

    Brian Dawson: Yeah, I do want that. I want protection. I want it to know where the vulnerabilities are and warn me. And I do think, Judith, that would be a prerequisite into this deploying code at rest, or truly applying dev AI.

    Alan Shimel: Fair enough. Hey guys, we are just about out of time. I think this was a great discussion. If there’s one thing I could take out of it though, is that the crystal ball remains cloudy in terms of how this is all gonna play out and we’re gonna have to wait for it to kind of come into focus as we – But it certainly will be an important part of software development, of operations going forward. And it’s gonna be an increasingly important part.

    Brian Dawson: [crosstalk]

    Mitchell Ashley: I think the crystal ball is in Magic 8-Ball, ask again later.

    Alan Shimel: That’s a good one, Mitch. But for now, we’re gonna call an end to this episode of DevOps Unbound. Again, thanks very much to Tricentis for their sponsorship. Thank you so much, Judith. Thank you so much, Brian, for appearing here and hope to see you on a future episode. Mitch, as always, great job, riding shotgun with me.

    Mitchell Ashley: Good to partner with you. Absolutely.

    Alan Shimel: Absolutely. But this is it for this episode of DevOps Unbound. This is Alan Shimel, have a great day, and we hope to see you soon on another DevOps Unbound, as well as don’t forget, every month we do a live round table open to you, our audience, with questions. So stay tuned for that as well. Take care, everyone. Bye-bye.

    [End of Audio]

  • Techstrong TV: Applications of AI in the Real World

    Techstrong TV: Applications of AI in the Real World

    Alan and Christelle talk about the “hello, Human” podcast series from FortressIQ where they discuss the latest topics in artificial intelligence and how it’s being applied in the real world. The video and a transcript of the conversation are below.

    Intro:  This is Digital Anarchist.

     

    Alan Shimel: Hey, everyone, welcome to another segment here on TechStrong TV. I’ve got a new guest who’s never been on TechStrong TV with us today. Her name is Christelle Flahaux – Flahaux – did I pronounce it right?

     

    Christelle Flahaux: You did, yeah, Flahaux, perfect.

     

    Shimel:                Okay. Hi, Christelle. Welcome.

     

    Flahaux:                Hi, Alan.

     

    Shimel:                Thanks for coming on our show, today. Christelle is the VP marketing over at Fortress, and she is gonna tell us about some – well, she’s gonna tell us about the company, number one, and then we’re gonna talk about some interesting podcasting she’s been doing. Christelle, we haven’t had anyone from Fortress, here, in a long, long time, so it falls on you, I’m afraid, to –

     

    Flahaux:                [Laughs] Give the update.

     

    Shimel:                Yeah, you’re gonna have to – well, before we even do an update, not everyone out here may be familiar. Let’s get’em familiar.

     

    Flahaux:                Sure, yeah, so I have actually been with FortressIQ for about a year-and-a-half, now; company’s been around for about four years. And we are in the business of delivering process intelligence. And what that means is, basically, helping large enterprises, large companies, find all the blind spots in their processes, right? Like, processes, the minute you deploy them, they go awry, right? Whether it’s human intervention, or outdated technologies, and so, we help folks kind of figure out what those processes are, and where there’s the ability for transformation. So, whether that be automation, whether that be retraining employees, process reengineering, but we do it from the human perspective, so we follow the human, not just the log file.

    Because not all processes happen just in Oracle or just in, you know, your ERP system. So, we connect all those pieces together, so we can figure out what the human’s actually doing.

     

    Shimel:                Love it. Love it, love it, love it. All right, and as we mentioned, you are CMO, there?

     

    Flahaux:                yes, correct.

     

    Shimel:                And you have been doing some podcasts – well, let me not put words in your mouth.

     

    Flahaux:                [Laughs] Well, so, yeah, so –

     

    Shimel:                What have you been up to, lately?

     

    Flahaux:                What have we been doing. Yeah, so when I started, you know, one of the things, because the company was so new and it’s a relatively new space, that is adjacent to a very hot automation space, right? Like, everyone’s heard of automation anywhere, UI path. So my big challenge was just getting the brand out there, right? Letting people understand what FortressIQ was about. And the other thing, too, was just education around artificial intelligence. I mean, I’ve been in tech for most of my career, but I think, you know, this new wave of artificial intelligence, computer vision, machine learning, I think a lot of people, especially over the age of 40, are afraid, right? Like, “Well, what is AI gonna do? Is it gonna replace my job? Is – ”

     

    Shimel:                Skynet.

     

    Flahaux:                Yeah, exactly, Skynet, Terminator, you know, the Borg, right? Like, what is this actually gonna do? And the whole concept of the brand came from, humans and technology are supposed to work together. And they can work together to take away all of those menial tasks, and all those things that get people down and, you know, that are mundane, and let the humans actually use their brains, right? Like what we’re supposed to be, and be the creators and be the innovators, and not getting bogged down in the tasks. So, it really was around creating this brand affinity to a company that’s gonna help you get comfortable with AI, right? And it was Covid, right? I joined the company middle of Covid, and podcasts seemed like a normal thing, right?

    We didn’t have events, anymore, we needed to get the brand out there in a way that would resonate, and so, we created this podcast with the idea of educating people around how AI is actually being used in everyday life, and how AI is being used for good. So that’s why we started it.

     

    Shimel:                Excellent. You know, I will tell you, not that I’m over 40 – well, I am, but – you know, AI’s gotten a bad rap, right? I think there is the Skynet Terminator people; there are other people who thin AI is like Steven Spielberg’s kid in the movie, you know, the AI movie. And, you know, maybe one day, but that’s not the AI that you’re talking about, that’s not the AI that we deal with a lot in our tech worlds. Most of the AI we do is a lot of machine learning, and then applying what we learned or from what patterns we see, and applying some intelligence to that.

     

    Flahaux:                Yeah, totally, totally agree, yeah. And that’s, I think, you know, digital transformation, everybody talks about it and, you know, people are going at it very different ways, and a lot of it’s failing, right? I think there was a stat out by McKenzie, you know, 80 to 90 percent of transformation projects fail, because I do think people aren’t looking at it as a big data problem, right? And big data, that’s where you start talking about the machine learning and everything, that it would take humans just years to go do versus a machine can be trained to go do it in weeks or even days. So, I think that’s the crux of it is people want to transform and they wanna embrace, you know, being digital. But, you know, this big data problem and how to attack it, you need things like machine learning and AI to go get it done.

     

    Shimel:                I agree, I agree with you. You know, I was at a KubeCon in LA, last week, Cloud Native Con, whatever you wanna call it, it’s the first conference I went to [crosstalk].

     

    Flahaux:                How was it?

     

    Shimel:                It was good. You know, the Linux Foundation did an amazing job of trying to make you feel safe. You had to have a vaccine to get in, right? No kidding, no BSing, if you didn’t have proof of vaccination, you did not get in. And then, once you were in, I felt like they made the space three times larger than it would’ve been normally, ’cause whether it’s the keynote room or the expo hall floor, everything was really spread out, really spread out. And you had to wear a mask inside. All. Everywhere. You didn’t wear a mask, security came over and said, “Put a mask on or leave.” And, you know, honestly, I thought they did the best they could do, and it was semi-safe.

    But the important thing was, the big buzzword, there, I mean, there had to be 80 vendors, there, you know, they have a lot of vendors. A lot of observability vendors, as they call themselves now. And really, observability is a big data machine learning kind of issue, right? Because observability doesn’t really get good until you have enough data to learn from, to see patterns, you know, to understand what the heck’s going on. And it just reminded me, again, that, really, at the end of the day, it’s always about the data, right? We’re seeing it in security, for instance, people who are getting wise say, “We gotta secure the data. We’ve been securing the applications, we’ve been securing the endpoint, we’ve been – we gotta secure the data.”

    Our data – who told me – one of the interviews I did, out there, the amount of data that’s generated daily, like, you know, petaflops or whatever, it’s just [crosstalk].

     

    Flahaux:                It’s insanity, yeah.

     

    Shimel:                Yeah, so without some AI machine learning, some – you know, and then, like, okay, this is data that’s near and dear, and we need to keep it in a very high premium kind of storage area. Other data, we need to do our analysis and we could offload that data to somewhere that’s cheaper. Other data, we can analyze and we don’t necessarily have to keep it. Who makes those decisions, right? How do we make those decisions? All of these things kind of go in there and it’s a crazy world, you know, it’s just – it’s not that it’s crazy. It’s a changing dynamic that we’ve gotta learn to manage and make actionable intelligence out of. ‘Cause at the end of the day, that’s what AI’s about, right? Analysis and taking action.

     

    Flahaux:                Yup, and making the decision. I always remember, so, I spent two, almost three years at MAPAR .

     

    Shimel:                Mm-hmm, sure, I remember that.

     

    Flahaux:                Yep, _____ _____ and lots of data scientists, right, on staff. And, you know, we had a database issue from a marketing perspective, and I wanted to clean out – talk about, like, a very small problem, right? – clean out a bunch of old contacts, ’cause I was, you know, they weren’t responding, it was old, and I didn’t wanna, you know, be on a blacklist. And I remember the head data science was, like, “But we have to save all that data.” And I’m, like, “No, we don’t, ’cause it’s changed,” right, half these people probably –

     

    Shimel:                It’s useless.

     

    Flahaux:                Yeah, it’s useless, like, they’re at a different company now, and it’s a wrong e-mail address, wrong address, and – and it was just this argument, right, kind of this philosophical argument about, “You have to keep the data.” [Laughs]

     

    Shimel:                [Crosstalk] it’s sacrosanct, right, like, it came down from the word of God and it should’ve been included in the Bible, you know, and – but, no, it didn’t, it wasn’t included in the Bible for a reason. It’s not, you know, it’s not the US constitution, either. We can get rid of it. These are people who’ve moved on from their jobs and those e-mail addresses don’t work, anymore.

     

    Flahaux:                Yeah.

     

    Shimel:                But let’s talk a little bit about running – is this the first time you’ve kind of hosted a podcast and got that all up and running?

     

    Flahaux:                Yes, no, it is the first time that we’ve started a podcast. I do remember, back in the early days, though, when I was at Arriba, and we had talked about starting a spend management podcast. And we did, and I don’t think it was very successful, I mean, I don’t think podcasts were that exciting, back then, or people were adopting it. But we actually sent out, you know, actual, you know, the old iPods.

     

    Shimel:                [Crosstalk], yeah, yeah, [crosstalk] people to listen.

     

    Flahaux:                The old iPods, and we loaded it with a bunch of episodes and, you know, engraved the back of the iPod. So, I still remember that campaign.

     

    Shimel:                And a little expensive to do that, kind of defeats the purpose.

     

    Flahaux:                Yeah, well, Arriba had deep pockets, back then, so it was okay for them to –

     

    Shimel:                I remember those days, Arriba – who was the flipside of Arriba _____ – yeah, crazy times [crosstalk] dot-coms.

     

    Flahaux:                Yeah, yeah, they’ve done well.

     

    Shimel:                Yeah, no, they have. Let’s talk about, like, how do you determine guests and stuff for your podcasts?

     

    Flahaux:                Yeah, so, you know, we didn’t think this was going to be a big podcast, right? We set out with a goal of having maybe 1,000 listeners, right, like, to start. And so, when we did the first initial list of who we wanted to go after, it was just people in our networks, right? Like, I know Kamal at Eightfold AI who’s doing some really great stuff with HR and talent management, and applying AI to it. You know, the podcast host, John, had a couple of friends, you know, that he knew that were in this space, from his time at the DoD, and obviously, _____, our CEO, was one of’em.

    So it was kind of a small list to start with, and we finished the first season and I looked at everybody who was on the first season and I was, like, “Wow, we have no women. None of these people on here are females.”

     

    Shimel:                [Crosstalk], yeah.

     

    Flahaux:                And it’s not like we did it on purpose, right? Like, we literally [crosstalk].

     

    Shimel:                No, quite the contrary, it’s on purpose, almost, that you have to go get women or other underrepresented communities. I mean, I run into it, frankly, all the time on our video shows, right? [Crosstalk]

     

    Flahaux:                Yeah, until you make it a priority and – so we wanted to do something for March, for International Day of the Woman, and I had an intern, who actually converted to a fulltime employee, and she’s passionate about this stuff. And so I just told her, I said, “Look, I wanna get four women, for March, right, like, you know, quick little month-long miniseries. Go get us four women.” And we ended up getting eight. People wanted to do it. And that’s the thing, from a podcast perspective, I’m sure you know this, if you ask, people are often [crosstalk].

     

    Shimel:                There are plenty of people out there, oh, absolutely. You gotta know who to ask is the issue, right? We, you know, we do – so we do a series of – besides this TechStrong TV kind of interview like this, we do 45-minute kind of longform video where we dive in, right, there’s Dev Ops Unbound, CISO Talk, the SRE Show, very nerdy geeky stuff, but it’s who our audience is and what we’re into. And our biggest thing is, once we identify someone, they almost always will do it. It’s just identifying. Go find me some women who talk about SRE stuff, right? Go find me some female CISOs. Well, there actually are a decent amount of female CISOs, now, that you can go to, but, you know, some of the other stuff, female QA testing and stuff like that, you know, it’s hard. It’s constantly a challenge for us.

     

    Flahaux:                Yeah, it’s hard, and I think now that, you know, we identified that we didn’t do it the first season, and we did this little miniseries and, you know, now I think, when we planned for this next season that just launched last week, it was, you know, “Let’s go cast a wider net and see what we get, and then prioritize.” We put on the show and we can always record and, you know, if we end up having – there’s no rules in podcasting, right, like, how many episodes need to be in a series, right, or how often you do it. And I think that’s the biggest lesson learned is there are no rules to this, right? So just generate content and get it out there and see how people consume it.

     

    Shimel:                Yeah, I try to do them on a consistent basis. Like, our cadence for a lot of the longform video – ’cause we do more video than audio, now, right, [crosstalk].

     

    Flahaux:                Okay, yeah.

     

    Shimel:                And we try to do every-other-week, right, I think that’s a good cadence. I mean, think about how you stream TV, like, you know, this is perfect. When streaming TV first came out, you could binge the whole season in a weekend, right, Netflix and chill. And now, most of the streaming channels don’t do that, they don’t let you do that, right?

     

    Flahaux:                No.

     

    Shimel:                They release a new episode every Friday [crosstalk] back to when we were one network TV and, you know, Happy Days was Thursday night, or whenever that was.

     

    Flahaux:                Yeah, and TGIF, right, you sat in front of the TV or – you know.

     

    Shimel:                Right, because you knew that was it. We’re almost coming back to that, I think. People, I don’t know if it’s a Covid thing, but people want that kind of regularity.

     

    Flahaux:                It’s something to look forward to.

     

    Shimel:                Yeah. Well, that’s totally right, you can schedule around, it’s not, “What do you wanna do?” “Oh, let’s just binge out, you know, and watch 20 episodes of this.”

     

    Flahaux:                Yeah.

     

    Shimel:                Anyway, _____ I wish you a ton of luck with this.

     

    Flahaux:                Thank you.

     

    Shimel:                It’s always good to see more podcast folks out there. Maybe this is something we could get – you know, we’re doing more and more distribution through TechStrong TV, so, that would be a pretty cool thing –

     

    Flahaux:                Yeah, no, I think that would be great.

     

    Shimel:                – we can talk.

     

    Flahaux:                Yeah, no, that sounds awesome.

     

    Shimel:                Well, welcome to the ranks, and continued success with it.

     

    Flahaux:                Thank you. Yeah.

     

    Shimel:                More importantly is, well, success with FortressIQ, right, it’s important stuff.

     

    Flahaux:                Yeah, it is, it’s fun, it’s definitely an exciting space to be in, so.

     

    Shimel:                Very cool. Alrighty – oh, wait, we didn’t tell people, where do they go listen to the podcast?

     

    Flahaux:                You can go to fortressiq.com/hellohuman, you can find us there, all of our episodes are up there. And if anybody watching wants to be on it as an AI and has an opinion, find me on LinkedIn, Christelle Flahaux, or on Twitter @mktgstella, marketing Stella, and we’ll get you on the show.

     

    Shimel:                Love it. Thank you, Christelle.

     

    Flahaux:                Thanks, Alan, I appreciate it.

     

    Shimel:                Alrighty. Hey, we’re gonna take a break, here, on TechStrong TV. We’ll be right back with another guest.

     

    [End of Audio]

  • Dell Updates AIOps Platform to Better Automate IT Ops

    Dell Updates AIOps Platform to Better Automate IT Ops

    Dell Technologies this week updated its IT operations platform infused with artificial intelligence, dubbed CloudIQ, to add support for its PowerEdge servers. In addition, Dell pledged to add support for its PowerSwitch networking hardware next month as part of an ongoing effort to extend the reach of its AIOps platform across the entire Dell EMC portfolio.

    At the same time, Dell next month also plans to employ webhooks and a REST application programming interface (API) to both integrate the CloudIQ cloud service with third-party platforms and share notifications and recommended actions via tools and platforms such as ServiceNow, Slack, Microsoft Teams, Ansible and vRealize.

    Dell has also defined an autonomous operations framework that defines the various stages of AIOps capabilities that organizations should be able to take advantage of as CloudIQ continues to evolve.

    Dell AIOps

    Drew Schulke, vice president of product management for Dell Technologies, said CloudIQ currently enables IT organizations to achieve level three of that framework. As Dell continues to update CloudIQ alongside delivering ongoing infrastructure upgrades, additional capabilities will be steadily added, noted Schulke.

    Dell is making a case for standardizing on both its infrastructure and the IT operations platform it provides. Whether acquired and managed by an internal IT team or consumed as-a-service provided by Dell, the goal is to convince IT teams to reduce their reliance on rival infrastructure platforms that are not as tightly integrated with CloudIQ. In effect, Dell is delivering continuous intelligence as an AIOps service, said Schulke.

    Of course, IT operations teams could opt to standardize on an AIOps platform that is integrated with infrastructure provided by multiple vendors. However, Dell is betting that an AIOps framework optimized for its platforms will reduce the total cost of IT in a way that IT organizations will not be able to ignore.

    Each IT organization will need to decide for themselves what path forward makes the most sense for them. However, as more AI capabilities are infused into IT operations platforms, it’s becoming clear that many of the tasks routinely performed by IT teams are becoming increasingly automated. Most organizations will need to make a decision in the months ahead about the degree to which they are comfortable waiting for their existing IT operations platform to be enhanced with AI capabilities versus acquiring a new one that might be more advanced.

    Regardless of the path forward, the level of cultural disruption AIOps will bring to IT teams will be high. DevOps teams, naturally, have always been at the forefront of IT automation. However, most of the recent advances in IT operations have generally been applied in a piecemeal fashion. Platforms such as CloudIQ are designed to be implemented on a more end-to-end basis.

    Thate level of automation will have a profound impact on everything from the number of applications an IT team can support to the roles of the individuals that make up that team. It’s not likely that AIOps will replace the need for those IT teams as much as augment them and redefines the job they are being asked to perform.

  • Is AI Taking Over DevOps Functions?

    Is AI Taking Over DevOps Functions?

    Artificial intelligence has taken the tech world to a new level of automation. Today, almost every specialization demands the intervention of machine learning to develop AI technologies that help businesses do more with less time and resources. Still, some organizations question whether leading with AI is a good investment. 

    For DevOps, the answer is a resounding yes. AI can enhance DevOps practices to accelerate the pace of software releases, helping businesses achieve continuous delivery. This enables programmers to release software about 10 times faster and allows programs to be reviewed before they are released. 

    AI also has improved DevOps culture, from enabling more efficient decision-making processes to code quality enhancements and automated operations. So, let’s take a deeper dive into how AI impacts DevOps.

    Recent Changes in DevOps

    Monitoring and managing all the data generated in DevOps environments involves a high degree of complexity, making it difficult for teams to collect and use data effectively. In addition, the volume of data any given team may receive can reach well into exabytes, so AI tools provide much-needed assistance. After all, analyzing massive data sets manually would take too much time for a human and won’t meet the demands of modern businesses.  

    What’s more, surveys show that 57% of developers in the United States have less than five years of experience. For this reason, software testing must be an ongoing and in-depth process to check and double-check that no vulnerabilities impact the security of the code. AI can supplement manual review to increase speed and accuracy in finding bugs. 

    That being said, integrating AI into DevOps functions doesn’t mean developers aren’t necessary, even though their roles have evolved considerably. Backend developers are involved in the development and operations sides as they take on ops tasks and tests, especially around cloud-based infrastructure and cybersecurity. And frontend developers will always be needed for the technical support of creative design—which no one can do better than humans. 

    In today’s dynamic and fast-paced world, developers need to release code faster than they used to while the operations teams ensure that the existing systems function with minimal disruption. This partnership is easier when integrating AI, as it makes the collaboration between development and operations teams more efficient. AI-powered systems provide a unified view into systems and glitches in the complex chain of DevOps. 

    Is AI Taking Over DevOps?

    Short answer: No. AI is supplementing human activity in DevOps to make things more efficient and secure. 

    Due to the complexity of DevOps requirements, people have been looking for a more automated solution that could assist humans in performance and accelerate the processes. What’s more, you can expect to pay around $60 an hour for an experienced backend developer in the U.S. This means using AI to automate the grunt work can be a big cost saver as well.

    Here are a few ways AI transforms DevOps functions:

    Enhanced Security

    Distributed denial of service (DDoS) and related attacks have become a widespread threat to the security of websites and online services these days. AI-powered tools have the ability to identify and mitigate these threats. In addition, AI can help in the pivot to DevSecOps in teams still using traditional DevOps.

    AI security tools detect anomalies and threats based on real-time data and by analyzing past behavior. In this way, AI has played a vital role in protecting many organizations, including schools and colleges, from cyberattacks by supplementing human security experts.

    Improved Data Access

    The massive amount of data that needs to be collected, organized and analyzed in DevOps is beyond human capabilities. However, AI has resolved this issue by compiling data from multiple sources and organizing it for analysis. This drastically improves and accelerates the process of DevOps.

    Rapid Software Testing

    Software development and software testing has accelerated with the integration of AI. As a result, various testing types used by DevOps, such as user acceptance testing, regression testing, and functional testing, are more efficient and accurate than ever.

    These tests produce a large amount of data that would take humans ages to identify and collect manually. With the help of AI, identifying patterns and coding practices that lead to errors and vulnerabilities has become convenient and fast. This data can then be used later by the DevOps team to increase their efficiency and improve development practices. 

    Timely Alerts

    DevOps teams sometimes struggle to react and respond to the alerts during incident response. With no priority tags attached, it becomes challenging for the team to prioritize incidents effectively. A more efficient, comprehensive alert system should be capable of spotting flaws instantly and marking the more severe ones as higher priority. That way, the team can tackle the issues more systematically and resolve them without failure.

    AI can assist in prioritizing the responses to alerts based on certain factors, including the intensity of the alert, the source of the alerts and past behavior. This leads to efficient management in situations where systems get flooded with massive amounts of data and have to respond immediately. AI-powered alerts are particularly important in DevOps security, so this is a critical benefit of AI in the discipline. 

    Superior Implementation Efficiency

    DevOps teams are often burdened by the managerial tasks of a rule-based environment which decreases their time spent in more innovative and creative areas. When AI takes over, these tasks become self-governed, increasing efficiency and reducing human intervention. AI systems can work on their own, sparing humans from these tasks to focus more on innovation and creativity.

    This way, AI has turned the DevOps into self-governed systems that no longer require the rule-based, human management of analysis. This should resolve the complexity of research that DevOps teams were struggling to achieve and enable faster adaptation.

    Feedback Loops

    The primary function of DevOps is to use monitoring tools to gather feedback from every stage of operations and then use that to improve software development and delivery processes. Performance monitoring tools use machine learning to gather information such as performance matrices, log files, datasheets, etc. The feedback collected is then used to track potential issues in advance and make suggestions to resolve them accordingly. The application is then altered by applying these suggestions to make it more efficient.

    Artificial intelligence is driving increased efficiency in DevOps functions. However, it would be a stretch to say that it is taking over, as there are still many areas that require human intervention. The idea behind AI integration is not to change the players but to change the game.

    Even though AI is assisting humans in accelerating software development, developers still lead many aspects of the craft. But it also seems impossible to perform DevOps functions at the required pace without the integration of AI. So, if you wish to release software at a pace that keeps up with modern tech trends, you need to incorporate AI in your DevOps functions.

  • SREs Say AIOps Doesn’t Live Up to the Hype

    SREs Say AIOps Doesn’t Live Up to the Hype

    What can you expect when investing in artificial intelligence for IT operations (AIOps)? Real-time visibility across huge volumes of information. Lightning-fast event correlation and anomaly detection. Automated remediation and self-healing, without Ops personnel having to lift a finger.

    It all sounds amazing—at least, if you believe the marketing hype. But do those kinds of AIOps benefits actually translate to the real world? According to a recent survey of nearly 300 site reliability engineers (SREs), not so much.

    Among SREs surveyed—including engineers for some of the world’s largest, most innovative digital companies—just 12% use AIOps as a day-to-day part of their monitoring toolkit. Nearly 40% never use it at all.

    Why aren’t current AIOps solutions living up to the hype? And what can this gap tell us about the state of IT monitoring in general? Let’s take a closer look.

    Sprawling Complexity

    In theory, AIOps should be a perfect fit for SREs. These are, after all, the people with one foot in the world of operations, one in development, whose job is to make distributed software systems ultra-scalable and reliable. A solution that can turn mountains of data about those systems into actionable insights should be exactly what SREs need. And, in fact, 53% of SREs cite lack of unified visibility across the stack as their biggest cloud application monitoring challenge.

    And yet, if we gauge by utilization, today’s AIOps tools don’t seem to be delivering on that promise. Asked about the value of AIOps, just 7.5% of SREs, on average, view AIOps as “high-value”—even less than those SREs (9%) who perceive its value as low.

    Why is AIOps so slow to catch on? Ultimately, the barriers facing these tools are the same as those facing human engineers: massive and growing complexity in IT environments. As digital products become more dependent on third-party cloud services, as the number of things businesses want to track grows (from infrastructure to application to experience), the sheer volume, velocity and variety of monitoring data has exploded.

    Diverse Data Sources

    Compounding the problem, enterprises increasingly rely on multiple “same-service” providers for IT services. That is, they use multiple cloud providers, multiple DNS providers, multiple API providers, etc. There are sound business reasons for doing so, such as adding resiliency and drawing on different vendors’ strengths in different areas. But even when two providers are doing basically the same thing, they use different interfaces and instrumentation, and their data sources often employ different metrics, data structures, and taxonomies.

    Whether you’re asking a human being or an AI-driven tool to solve this problem, this heterogeneity makes it extremely difficult to visualize the complete picture across the infrastructure. It also creates gray areas around how best to take advantage of each vendor’s different rules and toolsets. All of which makes automating anything beyond the most superficial operations tasks hugely challenging, if it’s possible at all.

    Optimizing AIOps

    AIOps really is the solution to this problem—or at least, an important part of the solution. Machine learning is, after all, tailor-made to analyze large volumes of observational and engagement data and react in real-time, as well as to continually identify opportunities for improvement. But before we can get there, we need to change the way we think about AIOps.

    Don’t try to do everything at once.

    It would be great if we could deploy AIOps, let it learn about our environment and watch it magically start remediating issues on its own. But we’re not there yet. (And if a vendor tries to convince you we are, you should definitely be skeptical.) If we break down our expectations into smaller, more manageable chunks, however, AIOps can accomplish quite a lot.

    Event correlation, topology analytics and smart alerting, for example, are all areas where AIOps can provide significant value right now. For these more limited objectives, AIOps can absolutely reduce tedious manual effort for human operators, reduce mean time to repair, improve visibility and more, and digital businesses should be leaning on AIOps to do that.

    Invest in AI and ML training.

    Part of the reason adoption of AIOps tools remains sluggish stems from the fact that many teams still just don’t fully understand how they can use them. The more SREs are exposed to AI/ML training, the more opportunities they will identify for concrete, meaningful applications of AIOps. For example, only 27% of SREs said, “Experimenting or receiving training to expand knowledge or skills” was a major activity as part of their job.

    Embrace “Platform Ops.”

    The biggest step we can take is to rethink how we use AIOps within an organization. Today, AIOps tools—like SRE resources themselves—are often dedicated to a specific digital product or service. As a result, enterprises end up trying to apply their AIOps tools, and the SREs using them, to support everything associated with that product (DNS, API, CDN, cloud monitoring, etc). As IT environments continue to get even more complex, though, businesses will likely find that shifting to shared platform tools and teams yields better results.

    Instead of trying to apply AIOps across every part of a digital service, imagine applying it to one part of that service (for instance, focusing on just DNS, or just APIs). In this way, the AIOps tools, as well as the teams using them, gain in-depth expertise across all the different interfaces and instruments used for that function and get continually better at normalizing multi-provider data sources across them. At that point, this platform-specific AIOps capability can then be scaled and reused across the company—providing more advanced DNS AIOps (or API, or CDN, etc.) for multiple digital products and teams.

    Now, you’ve got an advanced, highly specialized toolset for optimizing an important platform function, regardless of which digital product happens to be using it. You’ve narrowed the scope of how you’re using a given AIOps implementation while going much deeper into that function than a more generic, broad-based implementation could achieve. All of a sudden, even more advanced automation and self-healing becomes possible. And the loftiest marketing claims of AIOps start to look less like hype and more like viable real-world outcomes for your business.

  • How IT Can Use AI To Prepare For Uncertainty

    How IT Can Use AI To Prepare For Uncertainty

    The past year has been unpredictable for businesses – from migrating to remote work to planning for a return to offices to keeping pace with shifting government guidelines. With so much in flux, it was and is a good reminder that organizations must be prepared for the unexpected and have contingency plans in place.

    From an IT standpoint, this means ensuring that employees are well equipped to work efficiently and safely, no matter what work may look like in the year ahead. Fortunately, artificial intelligence (AI) technologies are evolving quickly, enabling enterprises to better manage an ever-changing hybrid workforce.

    The following are three AI-enabled strategies that organizations should implement to help prepare for times of uncertainty:

    Invest in Self-Driving Networks

    When companies quickly and unexpectedly moved their employees to remote work last year, networks experienced increased pressure on multiple fronts: usage, quality and issue remediation. These factors compounded on top of the fact that networks have continued to become more complex in the past few years as workloads become more distributed across public and private clouds. Even under the best circumstances, building and maintaining a traditional network requires a team of dedicated experts and can be time consuming. Due to their complexity and the increasing amount of data, networks are becoming more difficult to manage via command-line interface (CLI) and are required to adapt to changing workloads and employee connectivity – this is where AI comes into play.

    A self-driving network eases complexity by using AI and automation to run the network’s end-to-end user experience with minimal human intervention. This includes monitoring for issues such as bad cables and capacity hotspots within the network and solving them before they impact the end user. By ensuring networks are running smoothly on the foundational level, self-driving networks allow IT teams to spend their valuable time tackling more complex problems and driving innovation.

    Empower IT With AI Assistants

    Another way to free up the IT team’s time is to make a new hire – not a traditional employee, but rather an AI assistant. More specifically, an AI assistant that can answer questions and manage networks on par with a network domain expert – one that learns and improves as you work with it. As AI truly delivers on completing functions on par with humans, this is where AI and machine learning are disrupting industries.

    Today’s AI assistants, through conversational interfaces, are delivering more than simple “yes” and “no” answers to IT questions – they are able to answer complex open-ended questions such as, “Why is Bob having a poor Zoom experience?” They’re even learning to solve more complex problems, helping IT teams minimize the number of support tickets issued by frustrated remote workers. As businesses recognize the value AI assistants bring to their team, they now see them as members of the IT team that help them simplify the ever-growing complexity of IT networks.

    Educate Employees on the Benefits of AI

    In 2021, we expect the adoption of AI in networking to accelerate. Ensuring the right AI technologies are property executed starts with trust. Therefore, getting teams to perceive AI assistants as more of a teammate and less as a job-threatening technology is an important step toward successful AI implementation. The end-user experience is more important than ever, and AI is becoming more crucial for enterprises to manage this experience.

    To many employees, the concept of AI can be scary. A common misunderstanding is that AI will take over humans’ jobs. However, people are starting to understand the importance of AI by seeing it solve real problems. As companies implement AI technologies into their solutions, they should first prioritize open conversations about the business benefits of AI. By ensuring everyone is on the same page, teams can work toward building a better process together. After all, the goal is for AI to ultimately make their jobs easier.

    The last year has been difficult to navigate, but it serves as a reminder of the importance of planning for the unexpected – this is necessary to continue running a business efficiently and supporting employees remotely. Though there are sure to be more uncertain times ahead, we have the tools to prepare the best we can today.

    By taking steps toward building a self-driving network, investing in AI assistants and educating employees on AI, organizations can have peace of mind that they’re on the path to building themselves, their employees and their stakeholders a stable future that’s prepared for whatever uncertainties come their way.

  • Why Real-Time Asset Intelligence is a Must

    Why Real-Time Asset Intelligence is a Must

    Complexity breaks correlation. Intelligence brings cohesion. This simple principle is what makes real-time asset intelligence a must-have for AIOps that is meant to diffuse complexity. To further create a context for the user, it is critical to understand service dependencies and correlate alerts across the stack to resolve incidents.

    Why not just CMDB?

    CMDB systems have been useful to break down configuration items into logical layers. But that is not enough, because they can become outdated very soon.

    That is when connectivity among assets and their dependencies matter to remain relevant in real-time. This opens new possibilities for an enterprise to tap into. IT monitoring, change management, asset management and dependency mapping for troubleshooting are some areas that have a direct positive impact when enterprises have access to real-time asset intelligence linked to CMDB.

    If your CMDB is not accurate with data management systems, it is time you start exploring asset intelligence and asset discovery tools.

    AIOps asset discovery

    Why is Asset Intelligence Critical to AIOps?

    Asset intelligence is a critical part of AIOps. Unless you have a log and know about the assets that you have and how they are connected to each other, you cannot correlate to each other very easily. And that’s why real-time asset intelligence is required.

    Third-party asset and dependency intelligence is an obvious next step, but those come with more challenges and complexities. Depending on a third party for asset and dependency intelligence would require a continuous feed of data on a regular basis. This process gets expensive and obsolete quickly.

    Real-Time Asset Intelligence opens other Possibilities

    With automation and discovery tools, CMDB can be kept up to date, in real-time. Real-time asset intelligence with automation enabled by AIOps opens many possibilities, and it contributes many benefits to an enterprise. These include:

    IT Monitoring

    For alerts and notifications, just a monitoring tool is not enough. The information sent by such a monitoring tool is not up to date. People rely on manual feeds, which are expensive and are not synchronized. Asset intelligence brings forth deep information that builds context which would otherwise be missing. Asset intelligence can point to issues corresponding to servers, topologies, networks, storage, apps or services.

    AIOps dashboard

    Risk & Compliance Management

    An organization must audit IT assets periodically to eliminate risks from the end of life/end of support cycle, vulnerabilities and ensure compliance with regulations and local governing laws. IT asset information is also maintained in many different places and in various forms, in spreadsheets, databases, ERP or CRM, or other systems, which makes it difficult to gather this information. All these challenges can easily increase compliance and vulnerability risks for any organization. AIOps tools automate the process of data collection, analysis and reporting, significantly reducing the operations costs while improving the accuracy and timeliness of the insights.

    Change Management

    No one has a clue if anything changed in the IT infrastructure. And, even if something does change, no one will know unless updates are alerted and stakeholders notified. With asset intelligence, you will not only know about the change, but before making the change, the solution will also tell you if a change is – or needs to be – made to a business service, as well as that change’s impact on other dependent business services. This helps you evaluate the impact and take measures to contain the blast radius before making any changes.

    Asset Management

    Asset intelligence provides a 360-degree view of asset inventory, dependencies, life cycle, performance, utilization and compliance. It provides thorough insight into, ‘What do we have versus what is out of date?’ You get to compare those states and inform compliance.

    Dependency Mapping

    AIOps-enabled asset intelligence also allows for dependency mapping, which is especially useful for troubleshooting work. With machine learning and artificial intelligence, complex environments can be quickly and more easily deciphered, and troubleshooters would know where issues lie. Asset intelligence tells the troubleshooter the most complex part of the problem and what should be addressed or what needs more attention. It maps dependencies that are affected by alert correlation, triaging, planning, and ticket resolution.

  • Do You Need a Cloud AI Engineer?

    Do You Need a Cloud AI Engineer?

    For all the technological advancements we are witnessing in AI and machine learning, the practical benefits remain elusive in the non-tech business world, frustrating decision-makers as AI investment climbs. For example, over the past several years, many Fortune 500 companies have invested in strong data science teams and AI labs, but still struggle when it comes to producing and scaling their models. Moreover, a recent report by Algorithmia found that, while enterprise budgets for AI and ML are rapidly increasing for 83% of organizations studied, deployment challenges persist.

    These deployment issues often begin when leadership fails to exert enough upfront engineering focus to ensure their proof of concepts (POCs) are “real-world ready.” The challenges are compounded when businesses put these models into action using insufficient, outdated or cobbled-together IT infrastructures. This combination results in POCs that either fall apart under real-world conditions or are rife with inefficiencies. Fortunately, cloud tech today provides the opportunity to overcome these challenges.

    Businesses can enable development, experimentation and scaling of data science and AI solutions with more flexibility and speed than ever through cloud platforms. A design-led AI project that uses the tools available in the cloud can help organizations create business impact and realize ROI much faster.  So while the role of data scientist may have been the ‘sexiest job’ in recent years, we may be seeing a new ‘sexiest job’ title emerging: the cloud AI engineer.

    Why do we need a Cloud AI Engineer?

    Traditionally, a successful AI project in the cloud requires a team with different skills and titles, including data scientists, big data engineers, cloud engineers and full stack developers. This has led businesses to look at ways to merge these specialties into one specific title. Enter the cloud AI engineer who, theoretically, would be able to cover all these duties — including knowledge of and experience with the tools and accelerators that cloud platforms now offer, as well as demonstrated aptitude for fast-paced development in AutoML. These are skills and tasks previously spread out across other areas and roles within the organization.

    What is the Job Description?

    So what would this job actually look like? The cloud AI engineers of the future will focus on the deployment of AI and ML models at scale in the cloud, and on integrating them with existing products and IT systems. Additionally, cloud AI engineers would need knowledge of the different types of machine-learning algorithms; familiarity with frameworks like Tensorflow and PyTorch; hands-on experience with data processing; analysis and feature selection using analytic tools and ETL/ELT frameworks (Apache Spark, Hive, Amazon, Athena, etc.); and model deployment experience on cloud platforms like AWS, Azure or Google Cloud.

    Obviously, tackling all of these would be a huge undertaking for one individual; given the vast workloads on both the cloud and engineering fronts, it isn’t possible to replace these workers and their skills with just a handful of cloud AI engineers. Instead, cloud AI engineers will sit between the two disciplines and the workforces and will serve as a key bridge to easily deploy models and integrate them with enterprise systems and tools for consumption.

    Where are We Going to Find Cloud AI Engineers?

    Given that cloud AI engineers will have a robust level of responsibility, they will quickly become some of the most sought-after team members — but that also means talent for the role will likely be in short supply.

    Additionally, individuals with this broad skill set will not appear overnight — at least not en masse. Therefore, to build the cloud architecture capable of delivering the real-world AI results that businesses need today, tech companies need to find ways to break down the internal divides between data scientists and cloud engineers so that they can cross-train, learn and develop skills in each area. If this is done correctly, not only will tech companies have a more well-rounded workforce, but they will be able to deliver the practical AI success that non-tech businesses are looking for today.

    Despite excitement about AI and its benefits, operational challenges remain. By focusing on the cloud and building up specialists in this space, AI can begin to drive the practical results the business world needs far more quickly.