VMblog recently had the opportunity to sit down and speak with John Gentry, Chief Technology Officer, Virtual Instruments.  We talked about the company, and Gentry shared his industry expertise with us by explaining the current state of artificial intelligence, machine learning and AIOps, as well as how his company is approaching these technologies.

VMblog:  To kick things off, please tell us a bit about yourself, your background and your role at Virtual Instruments.

John Gentry:  I'm currently the CTO and SVP of Business Development at Virtual Instruments, a hybrid IT infrastructure management and AIOps company that focuses on optimizing the infrastructure supporting enterprises' business-critical applications. Our goal is to make it possible for enterprises to gain an end-to-end view of their entire hybrid IT infrastructure in the context of the application.

In my current role, I'm responsible for keeping up with and understanding key IT industry trends that affect product strategy and strategic alliances. I originally got involved in this space to solve customers' problems, and I started working with Virtual Instruments because I saw that they had the best data, the best analytics and the best technology that could solve real problems in IT operations, specifically from an infrastructure management perspective. The problem Virtual Instruments was solving was a problem that companies I previously worked with had struggled to manage - their solution was to throw more hardware and more people at the issue, which only made it more complex and resulted in finger-pointing across the organization.

VMblog:  Artificial Intelligence and Machine Learning have started to dominate the conversation around the digital transformation of IT operations, but many organizations are understandably still determining how to apply these concepts in practice.  So where are we in deploying these kinds of smart technologies in IT?

Gentry:  Most large enterprises are typically using dozens of management and monitoring tools - a combination of application performance monitoring, infrastructure performance monitoring, network performance monitoring, and an abundance of other siloed, vendor specific infrastructure monitoring tools. For the most part, the service management and change management tools work well, but the monitoring side is a major problem as the tools don't have a shared or common context and lack any awareness of the applications - they essentially speak different languages.

Some organizations have implemented first-generation dedicated AIOps tools that simply collect and analyze alerts or analyze logs from all these other products. These alert and log aggregation tools are helpful with troubleshooting, but they are all "post-facto" solutions that can't be used for real-time performance monitoring or for proactive problem prevention.

VMblog:  In your opinion, what are the biggest misconceptions around technologies like AI and ML?

Gentry:  The biggest misconception I see around these kinds of "smart" technologies is that many organizations see them as an easy button that can solve all of the problems associated with managing their increasingly complex IT infrastructure. But the reality is that, while some AI-based solutions have promised to make sense of the onslaught of data produced by modern businesses, you can't just layer basic correlation and de-duplication on top of existing legacy tools and have an effective AIOps solution.

When it comes to working with AI in complex IT infrastructure environments, it isn't just about throwing various algorithms and data at the problem. To get to a truly value-added application of AI or ML, you must understand the problem you are trying to solve, the context, and the right approach to applying AI/ML to derive specific outcomes. You must have "applied intelligence", sometimes called tribal knowledge, that provides a knowledge base of historical experiences that can then be applied to current issues. The combination of applied intelligence plus a specific combination of ML, statistical analysis and heuristics, is what second-generation AIOps platforms will include.

VMblog:  Let's go a bit deeper into AIOps - I've seen the phrase thrown around quite a bit by a number of different companies, but what does it really mean?  And how does it deliver value in the enterprise?

Gentry:  AIOps is a relatively new industry term that's increasingly gaining attention across the enterprise. It's described by Gartner as having two main components: big data and machine learning. True AIOps offers a plethora of benefits for the enterprise, including providing a real-time, end-to-end view of how a business' critical applications are performing and how the underlying IT infrastructure supporting them can be optimized. It provides a basis for both reactive and proactive problem identification and resolution. The key elements to AIOps include:

• Data ingestion: In order to make better decisions, you need the best data and applied analytics. AIOps solutions need to be able to ingest massive amounts of data from a myriad of sources.   Start at the source with data from individual elements of the infrastructure from AIX to AWS, systems of record from service management solutions, business context derived from application performance monitoring solutions, all with a real time architecture.

• Correlation: By bringing together all the infrastructure monitoring data with service desk and application performance monitors, true AIOps incorporates correlation across all domains to determine how to optimize application performance based on business importance and when there is an issue, the ability to determine what really is the root cause.

• Visualization: Comprehensive AIOps should provide easily understood dashboards and reports on the business' infrastructure that can be customized to any manager's requirements - from a simple red, yellow, green management dashboard to a full deep dive of components, IO metrics and capacity.

• AI-Based Machine-Learning: It goes without saying that true AIOps should feature next generation "smart" technologies, including AI-based machine learning. This is the only way these solutions can understand normal activity patterns and ensure you are only alerted when any element trends outside its normal activity. If you're constantly getting false alerts, your AIOps is not, in fact, true AIOps.

• Automation: In addition to recognizing abnormal activity, AIOps should be able to recommend how your IT infrastructure can be optimized and suggest courses of action to assure and improve application performance. IT should be able to proactively recommend changes to avoid future problems.

VMblog:  How is Virtual Instruments approaching this technology?  What differentiates you from others touting their AIOps capabilities?

Gentry:  What makes our VirtualWisdom solution different is the fact that our team is applying, quite literally, hundreds of people-years of experience to our approach to AI, ML and AIOps. By monitoring the entire hybrid infrastructure, we're able to generate our own unique data, as opposed to just pulling existing data sets from various external sources. In other words, we're not just aggregating data - we're generating and collecting it ourselves in real-time. This data is then used to educate the WisdomAI engine, allowing the intelligent algorithms to be fully informed and operational, rather than glorified pattern matchers. In addition, VirtualWisdom is an application-centric AIOps monitoring and automation platform. It knows where applications live on the infrastructure at any given time, knows the relative business value of the applications and how they stress the infrastructure.

VMblog:  AIOps is perceived as the prerequisite for true IT automation.  How do you see the relationship between AIOps and automated IT Operations?

Gentry:  Just like any nascent industry, smart technologies like AI and machine learning are going to continue to grow in their application and thus become harder to define as they become fundamental to all automated IT management tools. The technology itself will also move beyond just making sense of the data and informing humans to make better decisions, and ultimately it will enable proactive automation with self-driving optimization. However, the road to true proactivity and automation relies on the effective implementation and integration of AIOps with deployed monitoring systems and their ability to autonomously make decisions that lead to better business outcomes.  This will likely require a ‘crawl - walk - run' approach - the first phase of which I like to call ‘automation with governance'.  Make the recommendation, implement it and observe the outcome.  Then confirm or refine the approach until true autonomous automation can be unleashed.

VMblog:  So, what can we expect next?  What's Virtual Instruments' vision for AI, ML and AIOps?

Gentry:  Right now, we believe that implementing a strategic approach to AIOps is going to require an ecosystem-centric approach, which is why we are partnering with companies like AppDynamics and Cisco. At the same time, we are working with enterprise customers to understand how they can most effectively implement next-generation technologies like AI and ML in their drive for infrastructure solutions like AIOps. Without that understanding, it's going to be difficult for us to deliver and for businesses to reap the full benefits of the technology.

We're already seeing that AI has become more than just a buzzword, and 2019 is showing promise of being that year that we finally cut through that hype and can start having real conversations about how this technology is and can be used. Of course, we're interested in seeing how AI can contribute to automating IT operations, but we're even more excited to see how these technologies can help IT managers and their staff move beyond troubleshooting and focus on more valuable initiatives that advance the business. From improving efficiency and reducing infrastructure downtime, to being able to deploy key resources beyond tactical needs and putting more energy toward strategic tasks, we'll be able to see the true business impact of AI and ML-enabled technologies across the entirety of the organization.

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About John Gentry

John Gentry is an information technology professional with more than two decades of experience in product marketing, sales, and engineering. He's currently the Chief Technology Officer at Virtual Instruments where he's responsible for keeping up with and understanding key IT industry trends that affect product strategy and strategic alliances. John was named one of eight global leaders inducted into the Information Age Data 50 and has held various positions at mid-sized systems integrators, start-up collocations, managed service providers, and established storage networking companies.