Recently, Virtual Instruments’ CTO John Gentry spoke with the editors of AiThority to share his thoughts on smart technologies like AI and cloud-based ops platforms, the state of AI for IT infrastructure management in 2019, and how Virtual Instruments in leveraging artificial intelligence in our own operations. Of course, John can talk about this topic at much greater length than a blog post allows for, but here’s a sneak peek into some of his thoughts on the market:
Tell us about your interaction with smart technologies like AI and Cloud-based Ops platforms.
While some AI-based solutions, specifically in AIOps, have promised to make sense of the onslaught of data and alerts generated by the myriad of legacy tools by applying AI, they’re really just doing basic event correlation and deduplication with some rudimentary pattern matching. To me that isn’t AI. Just throwing math at the problem, without the right context or applied intelligence, isn’t the answer. I think we should change AI to mean “Applied Intelligence” instead of “Artificial Intelligence” – that would make a lot more sense and eliminate a lot of the hype.
When it comes to cloud-based ops solutions, I’m curious how they are going to evolve and if they’re going to be able to compete in a hybrid and multi-cloud world. While these kinds of solutions are good for monitoring operations on a single vendor’s cloud, I expect that they will run into problems once they try to perform across multiple infrastructure environments. How will they handle all different underlying infrastructure across legacy, private, and public multi-cloud environments? It’s a challenge of both scope and scale. I’ll be keeping my eye on these technologies to see how and if they change, because they will have to evolve to survive.
How does Virtual Instruments leverage AI in its operations?
When it comes to our work with AI, we realized early on that 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 have to really understand the problem and the context where you’re going to apply algorithmic intelligence – another phrase that I think makes more sense than “artificial”.
We’re applying hundreds of man-years of experience to our approach to AI, ML and AIOps. By monitoring and managing the entire 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. This data is then used to educate the AI engine, allowing the intelligent algorithms to be fully informed and operational, rather than glorified pattern identifiers.
Tell us more about your new approach to ending the “IT War Room”. How does Virtual Instruments deliver on its promises?
War rooms are a symptom of a much bigger problem within IT organizations. Siloed monitoring tools lack a common context and have no inherent understanding of the applications or their importance business. These fragmented tools can’t provide end-to-end visibility across IT infrastructure and applications, and teams within each silo can’t identify and resolve the problem on their own. This leads to “IT war rooms” filled with unconstructive finger-pointing.
Our hybrid infrastructure management and AIOps solution eliminates these obstacles by holistically monitoring, analyzing and optimizing the health, utilization, capacity and performance of IT infrastructure IN the context of the application. We apply real-time, AI-based analytics featuring machine learning, statistical analysis, heuristics and expert systems across the entire infrastructure, enabling organizations to proactively identify and resolve issues that impact business-critical applications, which in turn removes the need for the costly reactive firefighting that is the hallmark of traditional IT war rooms.
How do you see the raging trend of including involving AI and Machine learning in a modern CIO/CMO’s stack budget?
Hybrid infrastructures are continuing to grow in complexity and capacity, but the operations staff supporting those environments isn’t growing with it. Traditional tooling is something that every organization is trying to get away from, and AI offers a unique solution to that problem. Every company has a strategy for rationalizing every tool they currently use or plan to purchase, and every company is trying to find a next-generation approach to making buying decisions that will help consolidate the monitoring landscape and alleviate “tool-fatigue”. They are also looking for solutions that can off-load low level and remedial tasks from the limited staff to free them up for more strategic initiatives. There is already a palpable demand for AI-based solutions to address both these requirements, and the budget is there to support that demand.
What is the biggest challenge to Digital Transformation in 2019? How does Virtual Instruments contribute to a successful digital transformation?
Honestly, the biggest challenge of going through a digital transformation is keeping the lights on as you’re going through it. You need to sustain the business throughout the entire process, but how can you maintain the required level of service while at the same time trying to modernize the core applications and infrastructure?
We offer unique solutions that help companies accelerate their new business application and infrastructure deployments, all while de-risking their digital transformation initiatives. Our customers can determine which applications they should migrate to the cloud, evaluate new infrastructure products through performance testing, all while assuring the performance of the existing infrastructure that supports their mission-critical applications.
Where do you see AI/Machine learning and other smart technologies heading beyond 2020?
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 until they then slowly but surely consolidate. 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.
If you want more, check out John’s full interview on AiThority. To learn more about AIOps, check out our recent blog post here. And, as always, don’t forget to follow us on Twitter, LinkedIn and Facebook to stay up to date on the latest and greatest in AIOps for hybrid infrastructure management.