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New Relic Bets on AI to Advance Observability

New Relic is preparing to extend the capabilities of its observability platform by making use of both additional machine learning algorithms and the ChatGPT generative artificial intelligence (AI) platform.

Peter Pezaris, senior vice president for strategy and experience at New Relic, said during an interview on an episode of TechStrong TV that a prototype of an AI model created using machine learning algorithms will automatically notify developers when code in a production environment is not performing well—right within their integrated development environment (IDE). The notification will then take developers to the precise line of code identified as the root cause of the issue, he said.

DevOps teams will need to add two lines of code to applications running in production environments to take advantage of this capability. New Relic also plans to use a similar approach to integrate with generative AI platforms such as ChatGPT to create suggestions for fixing code, he added. ChatGPT now makes it possible to create entire programs in ways that output HTML and JavaScript code, so DevOps teams should expect to be able to use the platform to create natural language queries that invoke the proprietary query language New Relic makes available for its observability platform, he noted. Conversely, ChatGPT will make it possible to explain how a piece of code functions in plain language, Pezaris added.

Previously, New Relic employed a similar approach to integrate its observability platform with machine learning operations (MLOps) platforms used to build and deploy AI models. This was part of a larger effort to integrate MLOps and DevOps workflows, added Pezaris.

These AI extensions to the New Relic platform are part of a larger effort to extend the reach of the New Relic One observability platform further left toward developers. Most recently, New Relic added a free CodeStream module to its observability platform to give developers access to metrics and telemetry data that will enable them to write higher-quality code faster.

That approach eliminates the need to wait for IT operations teams to surface issues that usually don’t manifest until long after the code in question was originally developed. The overall goal is to provide development teams with frictionless access to observability data at every stage of the software development life cycle to reduce mean-time-to-detection (MTTD) and mean-time-to-resolution (MTTR) of issues.

Organizations should also be able to reduce technical debt faster by making it easier to identify issues that, for example, adversely impact application performance.

In general, most organizations are still in the early stages of achieving full-stack observability. A recent New Relic survey found only 27% of respondents have achieved full-stack observability, and only 5% claimed they have a mature observability practice in place. A third (33%) of respondents also said they still primarily detect outages manually or based on complaints, the survey found.

On the plus side, the survey also found nearly three-quarters of respondents said C-suite executives in their organization are advocates of observability, and more than three-quarters of respondents (78%) saw observability as a key enabler for achieving core business goals. However, more than half (52%) of respondents said they experienced high-business-impact outages once per week or more and 29% said they take more than an hour to resolve those outages.

The hope is, of course, that by augmenting DevOps teams with AI capabilities, the number of those outages will be dramatically reduced in the months and years ahead.

March 30, 2023
Perceptions of Reality

March 23, 2023
Postman Releases Tool for Building Apps Using APIs

Postman today made generally available a visual tool infused with artificial intelligence (AI) that makes it simpler to build software by invoking application programming interfaces (APIs) found on its network.

Shamasis Bhattacharya, chief software architect for Postman API, said Postman Flows makes it possible to use a low-code tool to build software using tens of thousands of APIs that can be found on the Postman API network.

The goal is to reduce the amount of toil required to build applications of moderate to medium complexity in a way that doesn’t require developers to have deep programming expertise, he added.

Instead, developers are presented with “blocks” that they can drag and drop to create an application and deploy the associated workflows. Postman Flows leverages AI to manipulate data returned by APIs using a natural language interface that reduces the level of technical acumen required to create those workflows.

That approach provides developers with a reactive experience that is embedded within a visual tool, noted Bhattacharya.

Many developers still have a natural bias toward writing code, but as demand for applications continues to increase, Postman Flows provides a means to significantly increase developer productivity, said Bhattacharya. There may still be instances where writing code is required, but as developers save more time using Postman Flows, many of them will switch over to a visual tool to build software faster by essentially gluing blocks together, he added.

There’s naturally a lot of focus these days on developer productivity during uncertain economic times. More organizations than ever are dependent on software, but their ability to hire armies of developers is limited. Low-code tools have emerged to provide a means for accelerating application development, but Postman is now looking to apply that concept directly to the network of curated APIs that it provides to application developers.

The one thing that is certain is that as the pace at which applications are being built increases, the number of them running in production environments will steadily increase. DevOps teams will find themselves maintaining a wide range of applications that were constructed using both procedural code and low-code platforms.

In the meantime, IT organizations may want to start evaluating which types of applications lend themselves to being built by development teams that may not require as much experience writing actual code. Much of the application development backlog organizations are struggling with today could be reduced using tools that automate a wide range of development tasks. That should free up developers with more advanced skills to work on more complex applications. That approach might also reduce developer turnover as much of the fatigue associated with building more routine applications is reduced.

Regardless of how applications are built, there will be a need to manage them in production environments. DevOps teams may soon be able to focus more of their time and expertise on optimizing application performance in production environments versus continuing to shepherd massive amounts of code through continuous integration/continuous delivery (CI/CD) pipelines.

March 22, 2023
Large Organizations Are Embracing AIOps

A survey of 265 IT leaders found the primary reason organizations are applying artificial intelligence to IT operations (AIOps) is to improve service and application availability and performance (60%), followed closely by automation of operations (58%) and processes (54%).

Conducted on behalf of OpsRamp, a provider of an AIOps platform, the survey polled 138 IT leaders working for enterprise IT organizations and 127 representing managed service providers (MSPs). The organizations surveyed generated at least $25 million in revenue and have more than 500 employees. The survey found monitoring/native instrumentation (55%) ranked as the most important feature of an AIOps platform.

Bill Talbot, chief marketing officer for OpsRamp, said the survey suggested the initial reason organizations appear to be adopting AIOps is to improve monitoring and overall observability as IT teams continue to find simple tasks—such as discovering what is in their IT environments—challenging to achieve.

Overall, the survey found the greatest IT operations challenge for enterprises in 2023 was automating as many operations as possible (66%), followed by driving greater efficiency and productivity of IT operations teams and processes (61%). Application-to-infrastructure dependency mapping is the top incident management challenge (64%), the survey found.

The primary operational benefit cited was reducing open incident tickets (65%), followed by reducing mean-time-to-discovery (MTTD) and mean-time-to-remediation (MTTR) tied at 56% and automation of tedious tasks (52%).

The survey also found 80% of respondents reported it took less than six months to implement an AIOps platform, with more than two-thirds (68%) noting it takes more than six months to hire engineers with the right skillsets for AIOps. Well over 90% of respondents said AIOps is delivering the level of value they expected.

Despite advances in AIOps, however, two-thirds of respondents said they are concerned about the accuracy of the data being used to train AIOps platforms. A little more than a third (36%) are also concerned about AIOps deployment causing job loss.

While there has been a lot of initial skepticism concerning AI applications in IT operations at scale, adoption has now crossed the proverbial chasm, said Talbot. IT teams are finding a wide range of static application environments that lend themselves to being automated using machine learning algorithms, he added.

Less clear, of course, is the degree to which AIOps can be applied to highly dynamic IT environments where changes to code are frequently made. However, the more routine processes that are automated, the more time DevOps professionals will have to manage more challenging issues.

At this juncture, it’s not so much a question of whether AIOps will be employed to manage DevOps processes as much as it is to what degree. It remains to be seen how generative AI platforms such as ChatGPT might be applied to IT operations, but it’s apparent that AI will automate a wide range of tasks. It’s not likely those capabilities will eliminate the need for humans any time soon, but it does mean that many of the rote tasks that make working in IT tedious may soon disappear.

March 16, 2023
How Open Source Can Benefit AI Development

Enterprises are increasingly reliant upon open source software. A full 95% of IT leaders say open source tools are key to their enterprise infrastructure. Simultaneously, we’ve witnessed a sharp increase in the adoption of artificial intelligence (AI) and machine learning (ML). Most IT divisions want to take advantage of AI in some form, and open source technologies will likely be vital to these efforts.

I recently met with Moses Guttmann, co-founder and CEO of ClearML, to understand the benefits of adopting open source software within the sphere of artificial intelligence. To Guttmann, open source has many benefits—the visibility it grants can equate to helpful feedback from the developer community, and transparency can inform new feature development and identify potential security gaps. There are also countless open source tools to help organizations develop and deploy ML models.

Below, we’ll consider the benefits and potential drawbacks of incorporating OSS in AI development and explore ways to optimize the use of AI within an organization.

Benefits of Using Open Source for AI

AI has evolved in leaps and bounds. What was pure sci-fi a few years ago has become a reality through ChatGPT and other AI-powered platforms. The capabilities of AI are awe-inspiring, but Guttmann believes it still requires fine-tuning to move from entertainment purposes into a valid business driver.

For example, custom ML models are difficult to construct from scratch, and generic APIs aren’t always that customized to the use case at hand to be valuable. Instead, Guttman foresees more and more organizations downloading open pre-trained models and retraining or customizing them to meet their specific needs.

For software providers, there are many benefits to adopting an open source strategy. The visibility it creates helps evolve a project with feedback from the community, said Guttmann. This, in turn, helps organizations build better, more stable products. For example, ClearML adopts plenty of OSS to power their user interface, backend and other components, in addition to providing a popular open source CI/CD framework for ML workflows. “We’re true believers in the power of community and like to work with open source and contribute back to these projects,” said Guttman.

Use Cases for Open Source in AI Development

Open source software is powering innovation in many ways. And when it comes to AI, there are numerous open source projects to help accelerate AI development. Such projects encompass pre-trained models, data-structuring tools, frameworks to train models and AI deployment pipelines. For example, many open source machine learning frameworks can help engineers build and train their models from scratch. These free-to-use tools include TensorFlow, Keras, PyTorch, H2O, XGBoost and others.

However, training models from scratch is time-intensive and complicated, said Guttmann. Instead, another option is to use pre-trained models accessible on GitHub or other repositories. Some popular examples are PaddleNLP and Natural Language Toolkit for natural language processing, OpenCV for machine vision and DeepFaceLab for deepfakes. There are also many open source Python packages that can structure and clean data for ML model training purposes.

Open source software can also be applied to streamline the ML DevOps pipeline itself. AI requires intense processing and is typically deployed to its own computing environments. Infrastructure management around running and training AI is a promising area where open source could assist, said Guttmann. “Automating this process with an abstraction layer to allow data science and ML engineers to access product-grade hardware is a must,” said Guttmann.

Potential Downsides of Open Source Usage

Yet, of course, there are some potential downsides to using open source software within the field of AI. Supply chain attacks are still a persistent threat, and numerous open source software risks can present themselves. These include bad actors compromising a legitimate package, name confusion attacks, unmaintained software and untracked dependencies. License restrictions could also potentially block the inclusion of OSS in certain projects.

Thankfully, the benefits of OSS appear to outweigh these concerns. Automated vulnerability scanning techniques can help detect known issues. It also helps that 89% of technology leaders see enterprise open source as more secure or as secure as proprietary software, according to a Red Hat report.

Tips For Getting the Most From AI

Open source is powering digital innovation on many fronts, including AI. But, simply looping in OSS isn’t the only aspect of fully leveraging AI—another will be the operationalization of ML management. The right automation infrastructure in place could enable the agility necessary to continuously retrain models. And since there are potential gaps in the CI/CD process, Guttmann recommended separating AI/ML from your normal CI/CD for security reasons.

Guttmann also believes that, in general, companies should be more transparent around ML models, including sharing details behind their creation and visibility regarding the datasets fed into them. Overlooking this could lead to ethical concerns. Furthermore, limiting the “hallucinations” generative AI produces is another huge gap that must be bridged, said Guttmann. This could be solved by simply allowing the system to respond with “I don’t know,” instead of confidently spouting nonsense.

In summary, open source could bring many benefits to bespoke AI/ML adoption. Many mature open source tools exist to enable engineers to create advanced AI and oversee the management and deployment of custom ML models. But the real test will be retaining this open creed by upholding visibility and transparency into the end creations.

March 16, 2023
Microsoft Power Platform Integrates ChatGPT: What This Means

Microsoft recently announced it integrated ChatGPT into its low-code Power Platform developer suite. This follows the news of ChatGPT-powered Bing search and Microsoft’s pledge to invest billions into its partner company, OpenAI.

ChatGPT, the large language model (LLM), has reached viral status due to its ability to generate impressively complex textual outputs given simple prompts. And now, enterprises are hoping to cash in on its power. Microsoft’s inclusion of ChatGPT in its Power Virtual Agents and AI Builder tools will likely signal more native inclusion of AI models within other low-code development environments throughout the industry.

However, not all is peachy in the world of generative AI. Users have compiled a laundry list of ChatGPT failures, ranging from simple arithmetic errors and inaccurate information to social biases and even one attempt to dissolve a journalist’s marriage. AI seems sure of itself, but the “hallucinations” it produces can be mind-bogglingly off-base. If such inaccuracies persist within production use cases, it could lead to false suggestions that confuse users and even harm a company’s reputation.

Below, I’ll consider the implications of this new paradigm of AI-driven development, exploring the benefits and potential risks of incorporating LLMs into low-code development frameworks. We’ll also highlight how this may upset the competition and consider things leaders should keep in mind as they seek to leverage this new technology.

Benefits of Incorporating ChatGPT

Low-code development platforms (LCDPs) excel at abstracting complex functionalities into usable components. They typically offer drag-and-drop capabilities and reusable templates to enable citizen developers and professional programmers alike. And there are many potential benefits of incorporating ChatGPT into such a low-code environment. For example, as a feature within Power Virtual Agents, ChatGPT can be used to create chatbots that connect to public company resources and internal company data. This could help users seamlessly develop a contextually-aware AI trained on a company’s documents.

Utilizing AI within low-code can spur development with deep inferences and guidance. Such AI assistance can drastically accelerate development efforts through enhanced auto-completion and intelligent insights, quickly generating boilerplate code and making suggestions more customized to the task at hand than your typical static templates.

GPT-based chatbots leveraging AI trained on a company’s documents could be incredibly more valuable than an off-the-shelf model without context. In the realm of customer support, an in-house trained AI could be beneficial in summarizing company information and automating manual support activities. Providing a low-code means to generate such chatbots also brings advanced AI to a broader range of users. All in all, this could advance the pace of development, especially for mobile interfaces, automated workflows and contextually-aware chatbots.

Implications For the Low-Code Space

In the wake of ChatGPT, the market has been scrambling to respond. Tech giants have debuted their own generative AI to mixed reviews. And some, myself included, are left pondering how low-code and AI will coexist in this new era. Will natural language-driven code generation replace the need for programming entirely, whether it’s traditional or ‘codeless’?

This remains uncertain. But what appears most plausible is that these recent developments will raise the bar throughout the software industry and augment how LCDPs are built and used. AI has the power to enhance LCDPs in a few ways, including augmenting the developer experience, training bespoke ML models and building more intelligent end-user experiences.

Yet, with Microsoft funneling billions into AI research and development, it could be challenging for mid-size low-code platforms to keep pace without their own AI-driven workflows. Thus, the LCDPs that do not integrate robust AI of their own may lose out on new subscribers. Plus, engineers hoping to create AI models that run on internal datasets may prefer working with a larger cloud technology suite in which their data is natively stored.

Downsides of AI-Infused Programming

ChatGPT and other generative models are absolutely impressive, but the results can’t be 100% trusted. And at the time of writing, ChatGPT usage within PowerApps is still experimental, which is indicative of ChatGPT and generative AI in general—still in the experimentation phase.

This doesn’t bode all that well for programmers relying on its outputs, given the potential for inaccuracies. Because although the results of ChatGPT sound authoritative, they are generated from knowledge scoured from the public web, which often contains bugs, errors and inefficiencies.

The outputs of ChatGPT may even suggest entirely nonexistent features! This is, unfortunately, a current issue with geocoding API provider OpenCage. ChatGPT routinely recommends users integrate OpenCage’s “phone lookup service” for certain prompts. But … OpenCage doesn’t even offer this feature. The team has received so many complaints from angry developers that they even had to issue a statement explaining what happened.

With AI code generation, the focus shifts from hardcoding toward creating and organizing prompts and debugging errors. And although it’s giving programmers newfound agility, it doesn’t solve all software development problems—namely, the DevOps side of the equation. There will still be friction in deploying code and piecing together an optimal architecture. Also, due to rising software supply chain vulnerabilities, code automation that loops in third-party dependencies must require careful security forethought. There’s also the chance of breaking changes and ongoing maintenance hurdles with any software.

Another important element is that low-code requires governance to be secured correctly. No-code business users may not have the security oversight to understand the implications of spinning up new services. And when you add AI into the mix, the technical implications escalate. AI can result in ethical violations, and bots have been found to communicate anger and irrational ideas when poked and prodded.

Are We All Just Hallucinating?

‘Hallucinating’ is when an AI model confidently spouts nonsense or inaccurate information. And although this is the case with some models currently, they are actively accepting feedback and being retrained. Over time, their outputs will improve. But for now, engineers should be aware that they are still under experimentation. As such, enterprises should use caution and test new AI innovations on internal processes first. And AI adoption must be governed carefully to avoid misconfigurations and abuse.

AI is here, and the future is now. But although it’s dismantling some coding barriers, developers still have their work cut out for them. This reality is a win for low-code solutions offering standard software delivery pipelines and centralized collaboration features. If LCDPs can maintain pace with AI and embed it into their workflows, both sides should fare pretty well in this new era.

Surprise — this entire article was written by ChatGPT. Just kidding! None of it was. I’m just tired of seeing this “gotcha” moment at the end of news segments…

This post was written by a human.

March 10, 2023
Before ChatGPT, AIOps Powered Enterprises for Years

Led by OpenAI and its ChatGPT software, a new era of consumer use cases for artificial intelligence is here. These exciting new tools have made AI more accessible to more users than ever before. No longer is this technology the exclusive domain of wealthy corporations.

However, while the world is now focused on the potential use cases for AI in consumer applications, it’s important to remember that this technology has been part of the business world for years. Behind the scenes, AI has quietly been powering solutions that companies of all sizes have harnessed to improve business outcomes. These technologies have specifically been used by IT teams in the form of artificial intelligence for IT operations (AIOps) to help modernize their companies and operate more efficiently.

By leveraging AIOps-powered solutions in use cases like observability, overworked and understaffed technology teams are improving their ability to manage complicated, hybrid and multi-cloud IT environments, automate processes and optimize their businesses. As companies prepare for a potential economic downturn and a reduction in resources, these solutions and AIOps are more important than ever.

Here are four ways that AIOps is helping businesses overcome today’s IT challenges.

Providing Unified Visibility Into Complicated Networks

Companies have accelerated their digital transformation efforts in recent years. This has included migrating workflows to public and private clouds, implementing new productivity tools and adopting next-generation technologies. The average business now relies on geographically dispersed workforces and IT environments that use 2.6 clouds and leverage more than 200 apps!

The result is that organizations lack visibility into these environments. A recent SolarWinds survey found that 54% of technology professional respondents stated they only have visibility into about half or less of their apps and infrastructure. This lack of visibility can lead to problems that negatively impact the return on investment for projects.

AIOps helps overcome this challenge by providing comprehensive visibility into an organization’s systems, regardless of whether those environments are hosted in on-premises, hybrid or multi-cloud environments. AIOps automatically collects and integrates insights and information across geographies, apps and infrastructures into one dashboard. This provides technology teams with single-pane-of-glass visibility into the state of IT environments.

Automating Processes & Remediating Issues

While the visibility provided by AIOps and observability is integral to managing today’s complex IT environments, the technology goes a step further.

With AIOps, teams can automate processes including the identification and remediation of issues. Given the stakes, this is critical. Companies must ensure their environments are always available and that their performance meets and exceeds customer demands. Even short service outages can lead to frustrated users and a damaged customer experience.

This is challenging, as an IT anomaly often results in many alerts and a flood of log messages, making the problem difficult for a human to understand and address. AIOps quickly ingests massive amounts of information, provides rapid root cause analysis, data/event correlation and incident management which allows for fast and automated remediation of issues as they arise.

Additionally, since this technology is always observing and gathering information on potential incidents, it can predict and prevent future issues proactively. By implementing AIOps, companies are taking a step toward autonomous operations or environments that can manage themselves without human involvement.

Protecting Revenue

In the face of an uncertain economy, every organization is taking steps to protect its revenue. With AIOps, enterprise technology companies have a powerful tool in this fight.

By implementing AIOps and observability, companies can quickly resolve and prevent IT issues before they arise. This ensures that IT environments are operating at maximum efficiency.

With AIOps, organizations can reduce downtime and prevent outages. This ensures that business-critical applications and infrastructure are available and functioning. These capabilities are more important as even short service outages have reportedly been capable of causing millions of dollars in damages.

Freeing Up Employee Time for Innovation and Efficiency

Over the last few years, companies have experienced severe staffing challenges, with many organizations finding it difficult to fill critical technology roles. This has led to smaller teams with more responsibilities and less free time. The result has been overworked technology professionals who spend far too much time on manual and menial tasks that do little to support the business’s bottom line.

By implementing AIOps, organizations can streamline workflows by automating repetitive and routine tasks. Doing so can cut down on the amount of overwhelming metrics technology professionals are required to analyze and, instead, empower those individuals and teams to again focus on innovating and completing projects that support key business objectives.

March 6, 2023
In DevOps, Chatbots are a People Problem

By now, you are undoubtedly aware that you can call up a chatbot or install an AI assistant into your IDE and generate entry-level code and deployment scripts.

Indeed, if you just go to a site like ChatGPT and enter “generate Python code to do X” where X is something you need to be done–or an example like “read a file and print out the contents one line at a time,” it will do it. And if you keep it simple, it will do it well. If you say “again, in Java,” it will comply and do it well.

Then say “new chat,” and tell it to “write a bash script to deploy an app named X written in Java and using Gradle to a cluster named Y,” it will do that also, informing you of the names of the deployment and service YAML files. Then you can say, “generate the deployment file mentioned,” and it will. And then you can say, “parameterize the Java version,” and it will do that, too. This iterative revision can continue until the deployment code, containers and deployment files are complete. If you have not yet, I urge you to spend the half hour going through these steps once required, so the potential is obvious.

It is not yet to the point where I would (or you should) trust it with complex systems, but all of that busy work we’ve been trying to overcome with templates and preconfigured containers? We can actually get around the bulk of it now. The same is true for testing of all kinds. There are tools that will auto-generate test scripts for a given set of source code. They will also generate test execution code. That means that the only step left—and the one that has held testing back because of the volume of work—is reviewing results … But wait, there’s more! In security testing, AI can help eliminate false positives. In performance or QA testing, it can filter out all but the most egregious issues (as defined by the org or by a larger pool of apps).

The problem we need to address today is the people issue. What we have traditionally used entry-level people for—and trained them in Dev/Ops/DevOps with—is no longer necessary. Because the AI can do it. That is stunning and really very cool, if you think about it.

But it heralds a danger that every org needs to be aware of. What we do and how we do it is changing. For the better, but change always brings uncertainty, and the foreseeable bit we need to confront is, “Now that we don’t need entry-level people for these steps, how do we recruit and train them?” I do not yet have a definitive answer for this question, because I am a product of my time, so I see things in terms of learning the bits that make up the whole—advising people interested in DevOps to learn at least one Linux command shell, to understand how networking works in containers, to write the scripts to get containers (and/or public cloud instances) up and running, etc.

I do think that every org should start using these tools at a pace that makes them comfortable because it allows us to continue our “more with less” growth without significant pain. But we need to make certain we are training the next generation of DevOps engineers in the process. For the foreseeable future, we’ll still be the ones bug fixing and troubleshooting, so we need to understand the environment and the applications. We are starting to get some sweet tools to help us at a higher level, though. Anyone who has inherited complex systems knows that the learning curve of tons of source files that make up many subsystems and all of the spaghetti code that these can be comprised of is painful. I don’t normally mention brand names here, so I’ll just say there is at least one tool out there that you can ask to “describe what this code does” and it will run with it and give you an answer in plain English. This might be the training tool of the future, with the ability to compare what you see in code with what the AI describes in English.

But for now, even if you don’t hunt down app description AIs, having entry-level people review the files that the AI generates will serve two purposes: First, to validate what the growing AI space is capable of and second, to get entry-level engineers into the entry-level bits of the systems.

And keep rocking it. Make sure you have a path for DevOps in the AI future, but remember that the creation of the calculator did not statistically change the number of mathematicians, just made the bulk of them more productive. That’s all we’re doing here, just a bit more flexibly … But in the end, like a calculator, the AI has an equation that it fills and reports back with.

March 1, 2023
Six Mainframe DevOps Predictions for 2023

DevOps, or the automation of application development and hand-off to operations, is more prevalent than ever on the mainframe. As we begin 2023, below are six predictions for what the coming year will bring. The composite themes will undoubtedly be speed, alignment and experience.

1. Enterprise DevOps Will Break Down Silos Across Core Systems

DevOps practices will continue to expand beyond cloud and distributed systems to include more core systems than ever before. Core platforms, including the mainframe, are the functional backbone of mission-critical applications that drive the world economy. In a recent IDC survey, 82% of organizations reported taking steps to prioritize using the same application development tools across distributed and mainframe teams. 2023 will continue to see a rise in enterprise DevOps that will bring down the silos between platforms. As core system workloads increase, organizations will see the benefit of aligning platforms such as mainframe and distributed to realize the full benefit of their DevOps initiatives.

2. Growth in AI/ML as Key Components for Measuring DevOps Performance Metrics

The value of analytics will increase as organizations emphasize the use of key performance indicator (KPI) data to measure the performance and maturity of DevOps pipelines. In this way, organizations will not only gain more insights into their pipelines, but will also be able to proactively manage the data in real-time. They will be able to adjust loads as needed and create smart pipelines with advanced error handling. Organizations will leverage benchmarks within the analytics to see how they compare with their peers.

3. Cross-Functional Pipelines Will Create Hybrid Automated Everything

This will enable the cloud, core systems and data to deliver a more holistic view of “platforms, processes and people.” Silos will finally disappear. Automation will become a key priority in this effort as organizations seek to free up time for developers to innovate and achieve greater velocity, quality and efficiency. A single integrated and connected pipeline will emerge that automates the creation and testing of applications in a single flow. The rationale of prioritizing this alignment between distributed and core systems includes the reuse of best practices, workflow simplification, cost-effectiveness, application integration and streamlined training.

4. DevOps Will Embrace Platform Engineering and Site Reliability Engineering (SRE)

Platform engineering is the next evolution of DevOps, taking a DevOps platform and turning it into a self-service product to improve the developer experience. It treats DevOps as a product and allows organizations to create an end-to-end offering that serves developers, operations and platform teams. SRE focuses on the “Ops” side of DevOps, specifically how to automate operations capabilities. This is vital to the DevOps mission since it provides the data that allows organizations to manage what they can measure.

5. DevSecOps Will Play an Increasingly Central Role

Security must become a central part of the toolchain and a “first-class citizen” in the pipeline. Static application security testing (SAST), also known as white box testing, along with dynamic application security testing (DAST), also known as black box testing, will be further integrated into processes to enable security teams to scan large loads at rest or in real-time quickly and efficiently.

6. Git-Centric Enterprise Source Code Management (SCM) Strategy Will Grow

Git has become the de facto standard for SCM for distributed application workloads. In a recent IDC survey of industry professionals across 11 countries and multiple industries, 87% said they will migrate all or some of their mainframe source code to Git over the next three years. This means Git and Git-native development will become important capabilities for mainframe DevOps companies to support. Organizations will increasingly prefer implementation approaches and tools that offer flexibility in both scope and pace.

The techniques highlighted in these predictions are, naturally, related. They point to an ever-increasing capacity for efficiency and quality that will enable core and distributed technologies to work better together.

February 24, 2023
DevOps Use Cases for AI-Assisted Kubernetes

As indicated in my prior blogs Optimizing Cloud Costs for DevOps With AI-Assisted Kubernetes and Optimizing Cloud Costs for DevOps With AI-Assisted Orchestration, an AI-assisted Kubernetes orchestrator is needed to optimize cloud costs for DevOps, DevSecOps and SRE. This blog describes DevOps-specific use cases for an AI-assisted Kubernetes orchestrator. The blog also describes a roadmap for implementing an AI-assisted Kubernetes orchestrator and the benefits of a solution using an AI-assisted Kubernetes orchestrator for DevOps.

Use Cases for AI-Assisted Kubernetes

An AI-assisted Kubernetes orchestrator can offer many benefits to organizations running containerized applications on Kubernetes clusters. Here are some use cases for an AI-assisted Kubernetes orchestrator:

1. Auto-scaling: An AI-assisted Kubernetes orchestrator can help automate the process of scaling up or down the number of pods based on the traffic or usage patterns of the application. The AI can analyze the performance metrics of the application and determine the optimal number of replicas needed for the application to function efficiently.
2. Load Balancing: Load balancing is a critical component of any Kubernetes cluster. An AI-assisted Kubernetes orchestrator can optimize the load balancing by analyzing the network traffic and determining the best way to route traffic to the different pods in the cluster.
3. Predictive Maintenance: An AI-assisted Kubernetes orchestrator can help identify and diagnose issues before they become critical. The AI can analyze the logs and performance metrics of the applications to identify patterns and anomalies. Based on this analysis, the AI can predict potential issues and notify the operations team.
4. Optimization: An AI-assisted Kubernetes orchestrator can optimize the resource allocation of the Kubernetes cluster by analyzing the usage patterns of the application. The AI can identify the optimal amount of resources required for each pod and allocate them accordingly.
5. Self-Healing: An AI-assisted Kubernetes orchestrator can automatically detect and recover from failures within the Kubernetes cluster. The AI can analyze the logs and performance metrics of the pods and take corrective actions to ensure that the applications continue to function properly.

DevOps-Specific Use Cases for AI-Assisted Kubernetes

An AI-assisted Kubernetes orchestrator can provide several use cases specifically for DevOps teams. Here are some examples:

1. Automated deployment: DevOps teams can leverage an AI-assisted Kubernetes orchestrator to automate the deployment process of their applications. The AI can analyze the code and configuration changes, build container images and deploy them to the Kubernetes cluster without any human intervention.
2. Continuous integration and continuous deployment (CI/CD): An AI-assisted Kubernetes orchestrator can facilitate the CI/CD process by automatically building, testing and deploying new code changes. The AI can monitor the code repository for new commits and trigger the necessary actions to build and deploy the changes.
3. Security and compliance: DevOps teams can use an AI-assisted Kubernetes orchestrator to ensure that their applications comply with security and compliance standards. The AI can monitor the security posture of the cluster and identify vulnerabilities or non-compliant configurations. The AI can also implement remediation actions to address the issues.
4. Disaster recovery: An AI-assisted Kubernetes orchestrator can help DevOps teams to recover from disasters quickly. The AI can analyze the backup data and restore the data to the Kubernetes cluster. The AI can also create a disaster recovery plan and test it periodically to ensure that the recovery process is reliable and efficient.
5. Resource optimization: DevOps teams can use an AI-assisted Kubernetes orchestrator to optimize the resource usage of the Kubernetes cluster. The AI can analyze the performance metrics of the applications and identify resource-intensive workloads. The AI can then allocate resources more efficiently to ensure that the applications run smoothly and cost-effectively.

Roadmap

Implementing an AI-assisted Kubernetes orchestrator for DevOps involves a series of steps to ensure a successful deployment. Here’s a feasible implementation roadmap for an AI-assisted Kubernetes orchestrator for DevOps:

1. Define your use cases: The first step is to define the use cases for the AI-assisted Kubernetes orchestrator that align with your DevOps team’s goals. Identify which areas of your DevOps process can benefit from automation and optimization using AI.
2. Choose a platform: There are several AI-assisted Kubernetes orchestrator platforms available. Research the options and choose a platform that aligns with your use cases and technical requirements. For example, CAST AI provides an AI-Assist for Kubernetes solution that is specially targeted to reduce cloud costs by as much as 60%
3. Plan your deployment: Plan your deployment by defining the architecture, infrastructure and resource requirements. You need to ensure that you have the necessary resources, such as compute and storage, to support your deployment.
4. Prepare your data: The success of your AI-assisted Kubernetes orchestrator depends on the quality and accuracy of the data. Gather and prepare the data needed for your AI algorithms to train and make decisions.
5. Train your AI models: Train your AI models based on the data you’ve collected. Ensure that you test and validate the accuracy of the models to avoid introducing errors in your deployment.
6. Integrate with your DevOps toolchain: Integrate the AI-assisted Kubernetes orchestrator with your existing DevOps toolchain, such as CI/CD pipelines, monitoring and logging tools and configuration management tools.
7. Test your deployment: Test your deployment in a staging environment to ensure that it meets your requirements and use cases. Run a series of tests to verify that the AI-assisted Kubernetes orchestrator is working as expected.
8. Deploy to production: After testing, deploy the AI-assisted Kubernetes orchestrator to your production environment. Monitor the performance and health of the AI-assisted Kubernetes orchestrator and make any necessary adjustments.
9. Continuous improvement: Once deployed, continue monitoring and optimizing the performance of the AI-assisted Kubernetes orchestrator. Collect feedback from your DevOps team and improve the AI models, infrastructure and deployment processes.

Benefits

There are many possible benefits of an AI-assisted orchestrator for DevOps, including:

1. Increased efficiency: An AI-assisted orchestrator can automate many of the repetitive and time-consuming test environment and deployment orchestration tasks in the DevOps process, allowing teams to focus on more critical and complex tasks.
2. Improved accuracy: AI algorithms can analyze data and make decisions more accurately than humans. By using an AI-assisted orchestrator, DevOps teams can reduce the risk of human error in their provisioning and re-provisioning processes.
3. Faster innovation: With an AI-assisted orchestrator, DevOps teams can automate many of the deployment processes, resulting in faster and more reliable deployments. This can help organizations to deliver new features and updates to their customers more quickly.
4. Better resource utilization: An AI-assisted orchestrator can analyze resource usage and optimize resource allocation, resulting in more efficient resource utilization. This can help to reduce costs and improve performance.
5. Enhanced security and compliance: An AI-assisted orchestrator can monitor security and compliance issues in the DevOps infrastructure orchestration process, identify vulnerabilities and implement remediation actions. This can help to ensure that applications are secure and compliant with relevant regulations.
6. Improved scalability: An AI-assisted orchestrator can help to manage and scale Kubernetes clusters more effectively, ensuring that applications are available and performant as demand increases.
7. Predictive analytics: An AI-assisted orchestrator can use predictive analytics to identify potential infrastructure issues and recommend solutions before they become critical. This can help DevOps teams proactively address issues and avoid downtime or other service disruptions.

What this means

As indicated in my prior blogs Optimizing Cloud Costs for DevOps With AI-Assisted Kubernetes and Optimizing Cloud Costs for DevOps With AI-Assisted Orchestration, an AI-assisted Kubernetes orchestrator is needed to optimize cloud costs for DevOps, DevSecOps and SRE. This article explained DevOps use cases for an AI-assisted orchestrator like CAST AI. Such an AI-assisted Kubernetes orchestrator can help DevOps teams to streamline their operations, reduce costs and improve the quality and reliability of their infrastructures and applications for testing and deployment. By leveraging AI technology, DevOps teams can focus on innovation and delivering value to their customers.

February 24, 2023