devopsy.kinsta.cloud

Category: AI

VMware Adds Analytics to Automate IT Operations Based on Intent

VMware is moving to make it easier for organizations to automate data centers and public cloud based on its core hypervisor technology by injecting advanced analytics into its VMware vRealize Suite, enabling them to manage IT environments based on intent rather than having to manually manage every process themselves.

At the core of those update is the Capacity Analytics Engine, which is being added to version 6.7 of vRealize Operations. Mahesh Kumar, senior director of product marketing for cloud management at VMware, said the capacity analytics engine makes use of Autoregressive Integrated Moving Average (ARIMA) techniques to forecast future system events based on historical data points that identify a long-term trend of sudden spikes in usage. Armed with that data, vRealize Operations can now provide cost analytics that include advice on ways to adjust application size and reclaim idle resources on either a public cloud or inside a local data center. IT organizations can also run “what if” scenarios, Kumar said.

On top of that capability, VMware is also now adding predictive analytics that enables IT organizations to define a set of intents such as workload balancing to reduce software license cost, prioritize one set of application to maintain a service level agreement (SLA) or make sure certain workloads only run on servers that have been shown to comply with a specific regulation or mandate. Tighter integration between vRealize Operations 6.7 and vRealize Automation 7.4 then can be continuously employed to automate DevOps processes in a closed-loop manner at higher levels of scale without having to add additional headcount, Kumar said.

In addition, the latest version of VRealize Automation includes more than 120 free, curated blueprints and OVF (Open Virtualization Format) files out of the box. VMware has teamed with Bitnami to add 20 new blueprints and 100-plus new OVFs of applications and databases including GitLab, Hadoop, Jenkins and MongoDB to speed application development and deployment. VMware also updated the Custom Form Designer and added multitenancy capabilities.

Finally, VMware vRealize Operations is sporting a new user interface and integrates with VMware Wavefront monitoring software.

Longer term, Kumar said vRealize Operations will be where VMware applies machine learning algorithms to enable artificial intelligence (AI) models to automate the management of VMware environments.

Collectively, Kumar said the VMware vRealize Suite moves IT organizations that have standardized on VMware in both their local data centers and in the public cloud much farther down the DevOps maturity curve. In effect, VMware vRealize Suite creates an “easy button” for managing complex VMware environments spanning multiple clouds, Kumar said.

VMware is also betting that AI models will make it a lot easier for organizations to make a cultural adjustment as the management of compute, storage and networking becomes more unified. It’s not clear how long it will take for those machine learning algorithms to manifest themselves in VMware management software. But it’s clear most IT organizations running VMware soon will need to rethink who is responsible for running what inside an IT operations team.

— Mike Vizard

April 4, 2018
The 2 Best Ways to Automate Your TechOps

The DevOps community is rapidly building tools and processes to automate more of the DevOps workflow. One key is to put engineers on the front line who can find root causes of problems and automate more of the common tasks. Here are the two key areas to focus on when automating operations processes.

Automate Your Most Expensive Tasks First

The first step is to do some analysis on where the team spends most of their time. You will want to know if they’ve done a good job around the tickets and how many requests of what type have they gotten.

After analysis, one company found it had more than 500 tickets a day for user access requests from users who were locked out of their account. This is a common problem, especially in midsize to large companies that are doing user management or account management and have very manual processes for onboarding and offboarding.

There’s a ton of money that can be saved by automating these processes. And there’s a whole host of tools for everything from full orchestration systems to pipeline stripping to make it possible. But the first step is to analyze where time is being spent on the repetitive, easily duplicated tasks and automating those.

Automating Monitoring and Self-Healing

There are a lot of different tools out there, and the amount of data that’s coming together is enormous. With the emergence of big data and machine learning, we have the ability to put all the different points of monitoring into one system and bring all the data together to provide a single picture of how everything is performing.

Data sources can include anything from systems monitoring tools to application performance monitoring tools to social media feeds such as Twitter and Instagram. In addition, it can and should include data from your IT service management (ITSM) tools. Once you’ve got that you can correlate your performance data with your incident data to actually predict when a problem is more likely to occur so you can fix the root cause or the underlying issue before it impacts your users.

There’s some really neat stuff that can happen with automation, whether that’s just a simple script that can save you time and money or advanced machine learning to actually predict issues and address them in real time. Take the first step to make your organization more efficient and effective.

— Sean Mack

March 22, 2018
The Role of DevOps in Building an AI-Ready Infrastructure

The successful application of machine learning for artificial intelligence (AI) requires several contexts to be addressed, primarily the technical, data and business considerations.

The Technical

From a technical standpoint, in building an AI-ready framework there are choices to be made such as which machine learning framework to select, the type and structure of learning function and the style of training algorithm to use. These decisions are normally based on the skill, experience and judgment of the computer scientist. A practitioner should be able to coordinate these aspects and focus on business outcomes without overly burdening the line of business with technical detail. The exception to this is when core design decisions in some way will impact business outcome.

The Data

Equally as critical to the efficacy of machine learning is the data available for use in development and training. A deployed algorithm is a function of the data inputs it received during training and testing; these inputs should be consistent and representative of the scenarios that will be subject to inference by the algorithm. It is difficult to accommodate so-called black swan events, as typically these require a level of adaptiveness beyond present machine learning technology.

However, we can still hope to achieve an effective and enriched algorithm that is appropriately responsive to real-world events by collecting and building data sets and training libraries that are truly representative of the environment in which the algorithm will operate. Ideally, machine learning data should include as many data outliers as may occur naturally within the real world—we should not be improving or self-selecting data, though granted most applications will use some form of normalization.

Speaking to data scientists and machine learning specialists, it is clear as individuals they have the technical skills and access to the core technology they need to do their jobs. However, more often than not it is access to and availability of real-world, suitably random data that is one of the biggest and most time-consuming challenges. Addressing this requires good engagement with the line of business, as access to sensitive data requires negotiation. But as matter of course, any business that utilizes data analysts to help inform critical business decisions and wants to benefit from machine learning should take the necessary steps to ensure it offers accessible and open data services to their machine learning teams. Having an open, accessible and agile data architecture is an essential prerequisite of being able to deliver an AI/machine learning-ready infrastructure.

The Business

On the business side, rather than address the how and what questions of technology and data, we also need to ask why. Certainly, as with any IT project, business does not want to engage machine learning or AI for its own sake. It is important to understand the business goals and seek new technology that will align with those objectives. This is where the application of DevOps techniques can greatly improve business outcome.

By aligning software development with software operations, more emphasis can be placed on what is important to the business while also helping to support the effectiveness and usability of new machine learning functions. In their deployment and use, these new applications should also exhibit many of efficiency features of modern IT technology. Thus, we expect new deployments to be able to make effective and efficient use of container technology. And where a particular function is infrequently required, how much more efficient is it to use a serverless function, perhaps with the data resident on private storage, and the function being called on demand in the cloud.

This type of capability is now readily available to machine learning thanks to the advent of virtualized GPUs provided by a choice of cloud services. This ability to dynamically and adaptively use infrastructure aligns well with the capabilities DevOps practitioners, who for a long now time have been used to managing the deployment of continuously developed releases of code. This agility, driven by the need for adaptability and innovation is very much what is required for successful machine learning and AI. But underpinning all of this is the need to maintain the conversation with the business around the open, yet appropriate accessibility to data.

— Matt Watts

March 22, 2018
IBM Advances DevOps for AI Applications

IBM today made its first foray into applying DevOps methodologies to building artificial intelligence (AI) applications. Announced at the IBM Think 2018 conference, the latest IBM tools and services for building AI applications include a Deep Learning as a Service capability that has been embedded with IBM Watson Studio development tools and Watson Data Kits that come prepopulated with data for specific vertical industries.

IBM also announced it has extended its alliance with Apple to combine IBM Watson machine learning with Apple Core ML to make it easier to build AI applications employing machine learning algorithms developed and curated by both companies.

Finally, IBM announced IBM Watson Assistant, a voice-enabled digital assistant that can be embedded into enterprise applications.

Ruchir Puri, chief architect for IBM Watson, said IBM is delivering a combination of services and tools that collectively will create a set of DevOps processes for managing the both the development of AI models and managing the complex data pipelines that need to be created to feed data into those models.

The IBM Deep Learning as Service offering is essentially a managed service that provides developers with access to hundreds of AI models based on neural networks that IBM has developed. IBM also revealed that the core technology employed to create the Deep Learning Service will be made available as an open source project called Fabric for Deep Learning (FfDL).

IBM also announced an open source Model Asset eXchange (MAX) online service, through which developers can discover free machine learning models, and the Center for Open Source Data and AI Technologies (CODAIT), which expands the mission of its Spark Technology Center to include building such models.

Puri noted that via all these offerings and services, IBM is moving to substantially lower the barrier of entry for building AI applications. Longer term, IBM also plans to make use of event-driven architectures enabled by serverless computing frameworks to make such models available on demand, he said.

Puri added that AI models require DevOps processes because, over time, organizations will find a need to either update or change the models as new data becomes available. In addition, AI is giving rise to a DataOps discipline for managing data pipelines that needs to become integrated with a comprehensive approach to DevOps, he said.

Puri said IBM expects AI models soon will be infused into every application being built or legacy application that needs to be updated. Given the scale of the task at hand, organizations will need to consider carefully how internal and external data flows into the models, which require continual access to data to learn. At the same time, organizations also will need to focus on ensuring the quality of that data to prevent machine learning algorithms from coming to the wrong conclusions.

Adoption of DevOps processes has been spotty so far. But as organizations begin to incorporate AI into their applications, many will discover it’s simply not feasible to implement AI without a robust set of DevOps processes in place to support the models.

— Mike Vizard

March 20, 2018
The ‘Blockbuster Effect’ in the Age Of Automation
Saying that new jobs will always be created to replace those that have been eliminated by automation is a mechanism for avoiding a real problem that’s lurking on the horizon.

In 2004, at the height of its popularity, Blockbuster Video had more than 9,000 stores and employed more than 80,000 people worldwide. About 50,000 of the total workforce was employed in the United States. Going down to Blockbuster to pick up a movie was a Friday night ritual for many, this writer included.

Blockbuster and other video rental stores, along with the advent of affordable Big Screen TVs, were significant contributing factors in the growth of the home theater. People now had an alternative to $10 movie tickets and battling weekend traffic to get to the cineplex. An entire family could view a recent movie in the comfort of their own home for about $5. The only thing that was needed to complete the movie going experience was the popcorn, which frugal families bought at Costco by the caseload. Those who were less thrifty could buy a box of microwave popcorn at the checkout counter of the video store. The $25 cost of a Friday night date was reduced to roughly $6.

Blockbusters kept people on the couches and out of the movie theaters. Television had already put a damper on movie-going. In 1930, 65 percent of the population went to the movies once a week. By 1964, the number dropped to 10 percent. Despite the introduction of 3D projection technology and movie theaters offering added features such as in-seat dining, movie attendance is now at its lowest point since 1992.

Blockbuster was a transformational business on the entertainment landscape. Today, the company is gone. Streaming technology combined with an “all you can eat” subscription model killed it. (See Figure 1.)

Figure 1: As Blockbuster stock declined, Netflix’s grew.

Just as Blockbuster did away with the need to go to a movie house to see a film, Netflix, Amazon and other video streaming services did away with the need to leave the house to get the evening’s entertainment. America stayed home and enjoyed movies on the internet for a fixed monthly fee. Blockbuster went out of business.

Netflix employs around 5,500 people with gross yearly sales of $11.69 billion. Blockbuster’s gross sales in 2004 were $6 billlion. It’s astounding that Netflix does almost twice as much business as Blockbuster did at its height, with less than 10 percent of the Blockbuster workforce. Such is the benefit of automation.

Whereas Blockbuster needed to staff a store in every neighborhood to do business, Netflix and other streaming services work from a central, virtual location without the added expense of redundant staffing. Blockbuster had 9,000 cash registers to man. Netflix has one, which is run by some sort of AI.

So, where did all the out of work clerks and store managers go? Hopefully, they went on to other jobs, maybe ones that were far better than the day-in, day-out tasks that go with processing video cassette rentals. After all, that’s the conventional wisdom: There will always be new jobs to take the place of the ones eliminated by automation. But, let’s face a cruel truth: It takes a lot more knowledge and skill to work on video delivery at Netflix than it does to check out customer purchases at Blockbuster. And Netflix requires fewer people to make the business go. Would a Blockbuster employee in Sioux City, Iowa, have the skills and mobility necessary to go on to to work at Netflix in Los Gatos, California? Seems pretty far-fetched.

Right now, things are pretty rosy in terms of the economy. So the conventional wisdom prevails: As automation eliminates one job, another new type of job takes its place.

Yet, there is another way to look at it. There are good arguments to be made:
- As automation eliminates jobs in the modern world, new jobs for humans will NOT be created at levels required to keep a workforce fully employed. Rather, AI will do more and require less.
- The lead up to large-scale unemployment will not be slow. It will happen fast, over a period of a year or two. And, it very well might happen within a decade rather than decades in the future.
Blockbuster’s demise came about pretty quickly. The company was at its peak in 2004. Six years later it filed for bankruptcy with $1 billion in debt. The music retailer Virgin Megastore closed its last North American store in 2009, a year after music streaming Spotify went public on the internet. (Spotify generated $3.8 billion in revenue in 2016 with an employee count of 2,162.)

Productivity due to automation continues to accelerate. In the early 1980s, it took 10.4 man-hours of labor to make a ton of steel. By 2014, the number was down to 1.9 man-hours per ton. The increase in productivity is due to increased levels of automation (think: robots). Given the choice between buying a machine and hiring a worker, companies are more likely to choose the machine. Thus, it’s entirely possible to imagine that the day when a few humans will be supervising the work of many robots making steel is close. Not too soon after that, a single human will be supervising an entire robotic workforce. Then, finally, as AI continues to improve and further dominate the production landscape, no humans will be required. The robots will supervise themselves.

How far off in the future is this time? Dunno. But I can tell you that robots are now assisting with surgery on humans. How long will it be until one surgeon can use automation to do the work of 10? Then, how long will it be until AI can do surgery unsupervised, five years? Ten years? Only 10 years ago driverless cars were still on the test tracks; today they are roaming the streets of Mountain View, California. An autonomous robot with a scalpel is no longer the stuff of science fiction. Once AI can do surgery unassisted, where will all the displaced surgeons find work? Maybe a few will go into research. What of the others?

At one time there were 200,000 horses in the City of New York. They provided the labor for a good part of the city’s transportation system. Today there are fewer than 1,000 horses. Cars, trucks, planes and trains have taken over. There’s no benefit to employing a horse when a car can do the work better. As General AI continues to develop and improve its ability to connect the dots as only a human can, how long will it be until the labor of most humans is not needed? What will it be like when there is no benefit to employing large parts of the human population because a machine can do the work better? Yes, the ramifications are scary. But, the possibility needs to be considered. The sad fact is that few people are considering such a scenario, let alone coming up with ways to address the impact on the horizon. Saying there will always be employment for humans is an avoidance mechanism, particularly given the fact that modern machines are going beyond purely mechanical labor and doing more thinking work.

In 1990, people thought there would always be movie rental until there wasn’t. In 2000, people thought there would always be a Virgin Megastore until there wasn’t. Today, Sears keeps closing stores. It will soon be gone from the face of the earth. Yes, Amazon is opening brick and mortar stores. This might be good news. But the fact that these stores are highly automated and have few employees cancels out the good feelings of the moment.

These days when a company comes to an end, it’s not a drawn-out process. It happens fast, over a period of a year. Many of the people left behind are confronted with low-wage, “show up and do it” jobs. The more educated have access to a pool of better paying work that comes with an excruciatingly steep learning curve. Whether the job is low-wage retail work or high-paying tech work, each worker is—or will be—competing against AI-powered automation that can—or will—do the job better.

It’s not a pretty picture. But it is one that can avoided if we decide to think about alternatives and put those ideas into action. Sadly, there are more people thinking about how to get to Mars than solving the problem of unemployment on a massive scale due to the proliferation of industrial automation powered by general AI. The problem is that we still seem to be stuck at, “There will always be jobs to replace the ones eliminated by automation.” To say otherwise is deemed Cassandra talk. Maybe so. But to not give the problem consideration is beyond folly. It’s a danger that will exact a heavy price from everyone who has to work to earn a living.

— Bob Reselman
March 19, 2018
Focusing on the Real Value of AI for IT Ops

In all the hype over AI, let’s concentrate on the real value it can provide, especially for busy IT Ops teams.

2018 is already shaping up to be the year when AI and machine learning (ML) go mainstream—at least in terms of public awareness, if not yet of actual usable products. As I write this, the annual CES event—the show that is, Prince-style, “formerly known as “The International Consumer Electronics Show—is in full swing. The usual suspects are out in force, with Amazon’s Alexa getting embedded in more and more products and Google also showcasing its own consumer AI capabilities.

More unexpected AI adopters included Ferrari, but there were also some notable failed demos, with an AI-enabled suitcase repeatedly running away from its owner or AI refusing to answer questions during an on-stage demo. All of this led the BBC to question, “When will AI deliver for humans?”

What Happens in Vegas, Stays in Vegas

Of course, much of what comes out of CES is frivolous, but there are all sorts of other applications of AI and ML. The problem is that whenever there is this much hype, disappointment and disillusionment are not far behind. Gartner’s famous Hype Cycle model can help here.

By Jeremykemp at Wikipedia, CC BY-SA 3.0

The conventional concern is that overly optimistic projects initiated at the Peak of Inflated Expectations risk failing, leading to excessive caution and fewer projects starting during the Trough of Disillusionment, before a more realistic understanding sets in and the technology achieves true mainstream adoption. Due to the nature of AI and ML, though, there is a second failure mode that is far more insidious.

AI and ML generally operates as a black box. Results are provided, very quickly and without the need to develop deterministic rules, but it is generally not possible to determine why a specific result was generated or troubleshoot failures in detail. As these techniques are used in the real world, potential problems that might not have been obvious in the lab begin to appear. If your smart fridge misreads the expiry date on that chicken (and you don’t think to check yourself), you might be in for an uncomfortable few hours. If your smart thermostat decides for some reason that your house should be as hot as physically possible, you’re still on the hook for the heating bill.

Those are the “nice” failure modes. If a facial recognition algorithm was trained on a data set that didn’t have the right racial balance, you might wind up with some of your employees not being able to get in the door, or some of your customers not being able to unlock your device, or whatever.

It gets worse. Law enforcement has an obvious interest in facial recognition—but what if a faulty system places you at the scene of a riot? https://www.liberty-human-rights.org.uk/news/blog/misidentification-and-improvised-rules-we-lift-lid-mets-notting-hill-facial-recognition Even if you can prove your innocence, you might still have to deal with some difficult conversations with your family and your employer, and your neighbours may never forget the time you were hauled off in handcuffs.

Don’t Throw The AI Baby Out With The Hype Bathwater

So should we just forget about this AI nonsense? Far from it. In fact, I am personally convinced that AI is very much a key part of our future. It just needs to be used in the right way.

I mentioned training data above, and that is the key. AI systems need a lot of good data, and they need to be trained to make good assessments. Out there in the real world, both sides of that problem are actually very hard. Gathering enough data, making sure that the data gathered are actually representative of the problem space, dealing with privacy issues around personally-identifiable information, and formatting the data correctly for ingestion by your machine learning system are already not trivial issues. However, even assuming that you have successfully dealt with that hurdle, the initial results will not be very good or reliable. Intelligent assessment by humans who understand the domain that you are training for is key if your deep learning model is not to go off the rails.

Come Up to the IT Ops Lab

Seen in these terms, IT Ops is an ideal domain to which to apply these techniques. First of all, IT infrastructure already generates huge amounts of event data – Big Data, if you insist – and all of those events are already, by definition, formatted to be easily machine-readable. Sure, there are many different formats out there, from SNMP, via various proprietary product-specific formats, to modern REST APIs, but it turns out that computers are pretty good at translating information from one representation to another.

Once the event data are in the AI system, it can apply whatever algorithms to try to make sense of them: which events are even significant, which ones are related to each other, what other relationships might exist, and so on. When it comes to evaluating the results, again, IT Ops already has people, tools and processes working on precisely that question: is this a real issue, and if so, what to do about it — or if not, why not. This evaluation can be fed back to the learning system to improve its results for next time, iterating over time to provide ever more valuable and reliable results.

This learning can even be applied to the evaluation process itself, watching what users find most interesting and valuable, and extrapolating from that to present the most significant data first, or looking for patterns in human interaction to assemble the most effective teams to work on particular types of incident.

This is what AIOps is all about. Don’t assume that the sort of consumer-grade AI that you see at CES is all that is going on in this field. AI has huge potential to help streamline enterprise IT Ops, by presenting human specialists with actionable events, helping them collaborate more effectively, and learning and improving over time.

Once the CES hype has died down, take a good look at AIOps and start to think how it can fit into your plans for 2018.

— Dominic Wellington

January 29, 2018
NASA/JPL Tech-Incubation CEO Talks Artificial Intelligence, Part II

This is the second installment of a two-part Q&A series on artificial intelligence (AI) with AJ Abdallat. It covers AI’s potential significance to computing; how AI affects jobs; the employee specializations needed to fuel AI; big data; and cultural challenges in the enterprise. Be sure to check out Part 1 as well.

AJ Abdallat is a serial entrepreneur who, from 1998 to 2011, worked with NASA’s Jet Propulsion Labs (JPL) and Caltech on a series of tech incubation startups. The goal was to commercialize NASA technologies, many of which were artificial intelligence-related.

In 2014, Abdallat co-founded and became CEO of Beyond Limits, a startup that secured licenses to NASA AI technologies for use in commercial applications. Beyond Limits currently holds exclusive licenses to 42 blocks of sophisticated intellectual property developed through NASA R&D investment, a $150 million AI technology head start under the aegis of Caltech’s Office of Technology Transfer and Corporate Partnerships program. Abdallat’s company is a leader in cognitive technology, which goes beyond conventional AI, binding deep learning and machine learning tools with symbolic AIs to emulate human intuition.

We interviewed Abdallat while researching the story, “AI Delivering on the Business Analytics Promise.” With AI poised to potentially revolutionize business, Abdallat’s career, which blends AI science and commercialization of the technology, makes his insights fascinating.

devopsy.kinsta.cloud: How does the advent of AI as used by enterprises compare to earlier significant computing milestones like networking, the internet, or cloud computing?

Abdallat: Part of me wants to quash as much artificial intelligence speculation as possible. The hyperbole. But frankly, I think AI is potentially more transformative than the other things you mentioned, which are just infrastructure. AI is starting to help us pose the questions that we ultimately want to answer. What’s unique about AI is that it’s an introspective exercise. Every new success for AI is giving us some insight about how we work.

AJ Abdallat is co-founder and CEO, Beyond Limits, an artificial intelligence firm. He’s an engineer and entrepreneur.

As we integrate AI, the trend may not be that transformative initially. Improvements will likely be focused on doing it faster, bigger with lower downtime and more efficiency. But what comes next is thinking about all the humans involved in that process, and what they touch and how they work. I think once AI starts to integrate with that activity, and act at that level within the enterprise, this will truly change how we think about what a business is meant to do. I’m a little biased in this, but you might go so far as to say we are entering the era of the cognitive corporation, where man and machine at all levels of the enterprise will operate in a synergistic fashion to solve problems, each learning from the other.

If you think about most business processes and most problems that humans find interesting to solve, they are heterogeneous. They move through a pipeline of different specialists that look at separate pieces or provide specific oversight to balance specialized objectives. If I can couple my engineering and sales activities at the enterprise level, and use AI to mediate the trade-offs of both of those objectives, and I have these people processes synchronized with machine processes, it gives me the ability to learn how these two potentially juxtaposed business ends are coupled, where they’re divergent, and what kind of switches I can flick or knobs I can turn to make the net positive of these two forces all the greater.

devopsy.kinsta.cloud: Is artificial intelligence intended to replace humans or help them do their jobs?

Abdallat: In some places, as with any kind of automation, you will see some jobs go away. But the labor that was there before finds a new role working with AI, and I think those new roles, especially at the level of knowledge work, will be very interesting.

As good as artificial intelligence systems are, they’re not going to set our objectives. They can move toward them, and think outside the box, which is one of things that we work hard to do at Beyond Limits. But being able to deal with the nuances of problems is still something that humans excel at. So, finding the things that are worth teaching to AI, to learn and to figure out, that’ll still always be a job in the hands of humans. I imagine that’s also when it gets kind of cyclical. I’m going to learn something, the machine’s going to discover a new pattern, we’ll optimize an approach that I didn’t think could be optimized to such a degree. That’s going to give me new questions, which makes me give it new training data, and the process continues like that.

devopsy.kinsta.cloud: What types of software engineers are now or soon will be in demand to support AI, machine learning and this new industry?

Abdallat: One of the things we’re starting to see is a change in job definition. You probably hear about data scientists and machine learning scientists. What we’re going to see more of is taking that science and flipping it with engineer. There’s a ton of stuff in the laboratory that works and is great. And scientists can write papers and come up with nice coefficients for polynomials that solve problems. But at the end of the day, there’s an infrastructure that makes all this work. So, we will probably begin to see new job titles emerge, like data engineer. A data engineer is maybe the bridge between the work that the data scientist does and the infrastructure in which his data lives. He or she is much more of a software engineer but has some knowledge of the data science workflow and algorithms germane to data and AI work.

Data engineers are becoming more important, and we’re finding they fit somewhere between IT and the business. I think that’s important. It’s the same with machine learning people. For example, the actuary of the future will simply be a data scientist who works in insurance. Systems analysts will gradually become systems data scientists. Of course, to make all this stuff work securely, we’ll probably see a ballooning of the specialized security people to support the work that goes into the systems engineering. Unfortunately, the world is more adversarial than we would like.

devopsy.kinsta.cloud: Does AI present a cultural challenge to enterprises?

Abdallat: Earlier big data solutions and the fact that AI looks promising for big data have paved the way. AI asks for similar machineries, although it can be used in smaller footprints when available. Culturally, some IT organizations are more progressive than others. Some like to play with new toys and would be stoked to get this down, assuming they have the budget, and AI may be enough to motivate them to allocate budget.

In other cases, there may be entrenched opposition to change. But it’s such an exciting field that whenever you start talking about artificial intelligence, you’re going to get buy-in. Some healthy skepticism is a good thing too. You can try to channel it into constructive ways of alleviating the concerns that people bring up, and assess whether they’re just nay-saying the next new thing or they’re aware of a valid institutional or physical barrier to success.

— Scot Finnie

January 25, 2018
5 Key 2018 Tech Predictions for the C-Suite

The most important digital trends for C-level business leaders to consider in 2018 relate to digital transformation, artificial intelligence (AI), internet of things (IoT), General Data Protection Regulation (GDPR) compliance and cybersecurity. Read on for insights and predictions about why these topics will matter so much to C-suites in 2018.

Prediction 1: Digital Transformation—All Aboard!

As one of the armaments of business innovation and disruption, digital transformation has become such a buzzword that it turns people off. But in 2018, fates likely will be sealed. A company has either transformed, is on its way to doing so, is actively planning its transformation or has decided not to get on that train. That last group won’t be invited to the AI party and are making themselves targets for disruption. According to Forrester, more than 60 percent of executives believe they are behind in their digital transformation. That’s OK, as long as they keep moving. CEOs need to fund this expensive endeavor from the outset and not try to make it pay for itself. Nickel-and-diming digital transformation could be the last mistake a CEO makes.

Prediction 2: By 2020, AI Will Be a Game-Changer

Every so often a technology comes along that has the potential to completely change everything around it. The last such technology was cloud computing. And like cloud, which had several precursors, AI has been around for a long time. AI and machine learning/deep learning are catching up to the initial promise, and that will become more evident as the next few years unfold.

The best options for near-term success with AI are business intelligence and decision support. Some enterprises are using machine learning/deep learning to plumb big data—especially unstructured data such as text and video—to drive new forms of data analytics.

Even so, many companies will strike out with AI/machine learning largely because it’s no paint-by-numbers endeavor. Companies need talented people who know how to harness this technology, but those people are scarce and don’t come cheap. Companies need businesspeople actively making sure that the technology serves the business need, potentially in charge of the project. And companies need the right data. An AI-based decision-support system, a common application of the technology, is fueled by quality data. Do you have it? This HBR.org article, “A Survey of 3,000 Executives Reveals How Businesses Succeed with AI,” will help get your organization oriented toward AI success.

AI is likely to be a largely experimental for the next year to two years as organizations give it a shakedown cruise. But early adopters have had some success, and many see potential for AI to deliver revenue growth.

Ultimately, AI and machine learning/deep learning will be looked upon as major advances with far-reaching effects, much like cloud computing and the internet. In 2018, it may not seem that way as enterprises experimenting with it feel the pain of separating the hype from the reality, adjusting business processes to make it work, cultural resistance from tech teams and a lack of available talent. Stick with it to reap eventual rewards.

Prediction 3: IoT—A Thousand Points of Entry?

Enterprise implementations of IoT functionality are very popular these days. But security vulnerabilities and issues are both inherent and legion with this technology. It simply hasn’t been thought through. In 2018, the disregard for IoT security solutions will come home to roost for several major corporations. Until the security issues are resolved, IoT will begin to be seen by some, at least, as a promising technology that’s not ready for prime time.

Prediction 4: The GDPR Powder Keg

By the end of 2018, some companies will be getting out of European markets, while others will look for a means of circumventing the EU’s GDPR. Still others will be scrambling to comply. And a last group will be doing what they’re doing now: taking the risk that the cost of the penalty is less than the cost of compliance. Forrester predicts that 80 percent of firms will not fully comply with GDPR. The unfortunate part is that, while a company may be able to ride out a steep fine on non-compliance, damage to its brand may be irreparable or very slow to heal.

Companies also can’t just throw money at GDPR—it probably will require new companywide policies and a shift in cultural thinking about personally identifiable data and the individuals behind them. Some companies are going to find it very difficult to make that cultural change.

Prediction 5: Cybersecurity Needs New Thinking

Customer experience and security are at odds with each other. Passwords don’t work. Biometrics aren’t completely safe. Security awareness is treated as an afterthought. Security breaches are an everyday headline. What’s it going to take to make society wake up? There is a huge need for a new type of user authentication system, as just one example. There are significant opportunities for those that can deliver new technologies, new products and new solutions. We need to think differently about cybersecurity. We need to teach it in schools. In 2018, we will hit bottom. It will become apparent to all of society that our security measures are antiquated and have no hope of meeting the rising threat from many quarters. As the digital world continues to assimilate more and more of our previous analog way of life at a rapid clip, security has trailed far behind. Cybersecurity desperately needs innovation.

— Scot Finnie

January 5, 2018
AI Delivering on the Business Analytics Promise
There’s a revolution underway in data analytics that could create a competitive advantage for companies that jump on it. C-suites, take note: AI-powered business analytics is finally delivering future-facing data points that CEOs need to guide their businesses to the sweet spot.

The artificial intelligence (AI) technologies machine learning and deep learning are being used today to power through mountains of big data, much of which is previously inaccessible unstructured data such as text, video, customer reviews and customer feedback. Data scientists analyze the trends the machine learning uncovers, resulting in human-vetted, AI-derived, predictive analytics that provide strong decision support.

Companies using AI-enabled business analytics are seeing positive results, yet most believe we’re just at the tip of the iceberg in utilizing AI’s capabilities in decision-making. Indeed, almost 70 percent of the CIOs who responded to a recent survey said they believe decisions made by machines will soon be more accurate than those made by humans.

“Machine learning and deep learning have fundamentally changed enterprise data analytics,” said Tom Wilde, CEO of machine learning solution provider indico. He pointed out that a large majority of data is unstructured; in fact, according to some measures, as much as 80 percent of enterprise data today is unstructured. “In many cases, the unstructured data is where the real signal can be found,” and, as such, AI identification and categorization techniques can be used to develop analytics that could be used to improve a company’s product, predict future sales, identify cross-selling opportunities and reach new markets.

“Machine learning supercharges business analytics and data science,” said Scott Zoldi, chief analytics officer at FICO. Machine learning algorithms explore relationships and data that even the best data domain expert may not think to explore, but, properly used, “Machine learning augments data scientists to make their discoveries seem superhuman. Data scientists that use machine learning will ultimately drive differentiated business value for their organizations.” In other words, AI is like using newly invented night-vision glasses for deep dark data—it should give a company a tactical advantage.

What C-Suites Should Know about AI and Analytics

AI and business analytics may seem like a panacea for what ails a business. But it’s not a plug-and-play technology—AI requires constant care and feeding to churn out the trends behind the data. Here are three things the C-suite must understand before adopting AI as a business driver:
1. AI in its current form is unlikely to replace a human being. It has transformative power, yes, “but AI is going to further a lot of progress that’s already in place, not necessarily upend everything,” said AJ Abdallat, CEO of AI firm Beyond Limits, an AI company. AI will be used to create great things in years to come, but it’s not a magic bullet, it’s not infallible and it requires humans in great measure today. People should not be concerned about their jobs, nor should there be any expectation that AI might save money on personnel. These cultural concerns, along with a not uncommon resistance to change, are one of the barriers to entry.
2. If your company punted on digital transformation, you’ll probably need an outside vendor to help you foray modestly into artificial intelligence. “The sooner you can modernize your digital infrastructure, the sooner you can bring AI in to address your [larger] business issues,” said Abdallat. In many ways, the ability to employ machine learning and AI may wind up being looked at as one of the main positive outcomes of digital transformation. Working in concert, these technologies are probably your best protection against sudden disruption by a digital-native startup with good ideas.
3. “Too many CEOs delegate AI to their innovation teams, who conduct small-scale experiments. It’s important for CEOs to bring their own AI vision to their organizations,” said Gurjeet Singh, executive chairman and co-founder of Ayasdi. CEOs need to apply AI to the most pressing problems facing their businesses. “The world is going to change far faster than they can grasp. They need to set course immediately.”
Adopt AI Soon

One thing that has become clear as more companies join the AI fray, either as purveyors or consumers of the technology: The time for digital infrastructure improvements and adoption of AI is now. That said, the scope of enterprises that have already adopted AI is comparatively narrow. Companies currently using artificial intelligence to explore big data include the obvious: Amazon, Apple, Citi, DoCoMo, Facebook, Google, HSBC, IBM, Lockheed, Microsoft, Mt. Sinai, Tesla and Twitter. Many other companies are experimenting with AI in one facet of their business.

How significant a trend will the use of AI by enterprises be? Is it at the scale of networking, the internet and cloud computing? “AI will have the same or greater impact on businesses for the next 20 years,” Wilde predicted.

“Part of me wants to quash as much of the AI speculation as possible. The hyperbole,” Abdallat added. “But frankly, I do think that AI is potentially more transformative [than the internet, cloud etc.] … I know I’m a little biased in this, but we might be entering the era of the cognitive corporation, when man and machine at all levels of the enterprise will [cooperate] to solve problems, each learning from the other.”

— Scot Finnie
January 2, 2018
The Impact of DevOps and Continuous Testing in 2018

At its core, DevOps is meant to touch an entire organization; however it’s heavily weighted in the software development life cycle (SDLC). That’s why DevOps is going mainstream in 2018. Companies are making the business benefits of DevOps a top priority—it’s all about delivering value to the customer faster through maximum automation of the release cycle/processes. And that begins with organizations realizing that to mature their DevOps processes, they need continuous testing as part of their continuous integration practice.

Cross-platform Engagement and IoT Will Mean More Devices—and More Apps

With organizations shifting processes and innovation to a mix of digital technologies—a concept known as digital transformation—consumers are engaging with different apps across various platforms 24 hours a day, seven days a week. Add in the rise of internet of things (IoT) devices, which allow for seamless transitions across smartphones, TVs, tablets and other devices. Now is the time for optimum user experience (UX).

In 2018, industries such as financial services, health care, retail and automotive will fully embrace IoT. We may also see new types of applications replacing current ones, as consumers and developers embrace the constantly evolving innovative capabilities coming in 2018. However, until the C-suite implements the tools needed to keep up with the growing demand for cross-platform upkeep and testing, other industries will experience slow adoption.

A critical step in delivering flawless UX is testing, and lots of it. Never has there been more to test, measure and develop than in this digital revolution we are seeing today.

Frequent New Releases Will Require Even Faster Updates

As the number of platforms consumers engage with rapidly grows, so does the pressure for developers to ensure apps are running as they should at all times. Same goes for the pace of cloud adoption: Cloud test labs will need more sophisticated capabilities, meaning more platforms and blind spots which require continuous optimization to ensure market readiness. This need for faster turnarounds on update releases and bug fixes will multiply in 2018.

To maintain and grow customers and profitability, CIOs must recognize the importance of empowering developers with the tools and time to implement continuous testing across the SDLC. Tools, such as those powered by automation and the cloud, increase efficiencies and free up time developers previously spent on manual quality-checking, allowing them to ensure the apps they are producing are keeping up with consumer expectations. If users feel their app is not delivering on the experience they want, they will delete it and move on to the next competitor, which has a direct negative impact on the company’s bottom line.

Outdated Technologies Will Make for Even Slower Updates

There is a suite of new tools being introduced to the market for all stages of the SDLC in the hopes of keeping up with the heightened speed of testing. As many DevOps environments struggle to adapt their pipelines to these new tools, many are powered by emerging technologies including machine learning and automation. In 2018, developers should ditch outdated infrastructure and processes in favor of more modern architectures, including implementing microservices.

Modernizing pipelines will allow developers to design in next-gen engagement methods such as artificial intelligence (AI), augmented reality (AR) and virtual reality (VR) without compromising velocity.

AI Will Experience Its Shining Moment (for Some)

In the coming year, AI will start to move from a theoretical concept to a commonly applied technology, especially within the DevOps space. However, developers first must understand what they want AI to help them accomplish—and how—within the SDLC and across the DevOps pipeline. One logical place to start is figuring out how they can best leverage it to analyze test automation strategies.

The value of a well-oiled, or not-so-well-oiled, DevOps process can make or break an organization. As the new year begins, be sure to include the IT leadership in these conversations to ensure developers are getting what they need, when they need it, to keep business moving as usual. IoT, AI and the cloud are just a few things to keep in mind as the industry moves to implement the latest tools in 2018.

About the Author / Eran Kinsbruner

Eran Kinsbruner is the lead technical evangelist at Perfecto and the author of the “Digital Quality Handbook.” He is a software engineering professional with nearly 20 years of experience at companies such as Matrix, Qulicke & Soffa, Sun Microsystems, General Electric, Texas Instruments and NeuStar. He holds various industry certifications such as ISTQB, CMMI, and others. Eran is a recognized mobile testing influencer and thought leader. He is also a patent-holding inventor (test exclusion automated mechanism for mobile J2ME testing), public speaker, researcher and blogger. Follow him on Twitter, connect with him on LinkedIn and check out his professional mobile testing blog.

December 11, 2017