Cisco Live 2018: How ScienceLogic's SL1 uses AI and machine learning to improve mission-critical app performance | Tech News

Murali Nemani, CMO at ScienceLogic, spoke with TechRepublic at Cisco Live 2018 about how the company’s SL1 AIOps platform uses AI and machine learning techniques to analyze data from across their customers’ network and data centers to improve the performance of mission-critical systems. The following is an edited transcript of the interview.

Murali Nemani: Being in the Bay area myself and being an ex-Cisco employee, I totally understand the Cisco ecosystem, especially of what they do for their partners. And so a lot of what we do at ScienceLogic is to take, let’s say, the digital transformation initiative that enterprises are pursuing and try to bring a lot of meaning to the operational data that they’re trying to build these platforms on. As digital transformation evolves and these mission critical applications are being relied upon for the core of their business, those applications are fundamentally only as good as the underlying infrastructure that is supporting them.

Our partnership with Cisco has been to take a lot of their application-rich capabilities through some of the AppDynamics investments that they’ve made, and their core infrastructure in investments that they’ve made through data center, the network, which they’re really well known for, as well as their cloud investments, and to be able to connect those two together with a set of operational data that gives a lot of insight to the performance of the applications, the mission critical applications, that CIOs and customers are depending on building their business on.

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At ScienceLogic, we introduced a product called the SL1 recently and that’s what we’ve been showing at Cisco Live. SL1 fundamentally applies AI/ML techniques around this whole initiative called AIOps, and looks at a lot of the operational data coming from multiple places within the IT ecosystem, the data centers, whether they are data centers that you own yourself, or data centers that are public data centers. Then be able to stitch together an operational blueprint of what’s really happening with the performance of your application and your network.

By that, you’re able to connect that insight to take actionable outcomes, right? That’s where the AI/ML elements come in, where this data feeds advanced neural nets and AI, or platforms like IBM Watson, to start looking at predictive analysis of where, let’s say, a performance degradation in the application happens, where that root cause is. How do you identify it and maybe even look at pattern recognition around issues that are about to happen that’s going to impact that mission critical application? Then be able to go put in remediations in advance of it. That’s what we’ve been working with from a product perspective, the capabilities we’re bringing to the market, and then subsequently how we’re partnering with Cisco and their customers to bring that capability to their service.

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