At last week’s ONUG Spring 2018 event in San Francisco, I moderated a panel discussion on re-tooling IT operations with machine learning (ML) and AI. The panelists provided a view “from the trenches,” sharing insights into how their organizations are applying ML and AI today, each in different operational domains, but with a common theme of overcoming the challenge of managing operations at scale.
Common Themes from the Panel
All three panelists stressed the need to have a clear understanding of your organization’s business objectives because these will determine how you source and curate the data that will be collected and analyzed. They all recommend starting with the low-hanging fruit — statistical analysis of time series data — which can yield immediate operational efficiencies.
With a wealth of ML and AI technology in the public domain, it came as no surprise that each organization relied heavily on open source software across the entire ML and AI software stack. However, while open source tools are freely available, the people who know how to use these tools are generally not. Therefore, each organization had to hire additional staff with relevant expertise in ML and AI, and the message was to be prepared to make a similar investment.
By eliminating time-consuming, labor-intensive tasks, there is legitimate concern that ML and AI will lead to the elimination of jobs. However, Verizon’s Bryan Larish offered a different take. He used an analogy inspired by the Marvel Comics character Tony Stark, who is transformed into a superhero with special powers while wearing his Iron Man suit. Verizon intends to make its network run better by augmenting the capabilities of its operations teams with ML and AI, transforming ordinary operators into a legion of extraordinary Iron Men. Hard to argue with that!