Artificial intelligence was once a magical concept, the stuff of science fiction. Now, after decades of research and commercialization, it’s just another foundational tool to keep the enterprise stack running.
Nowhere is this more evident than in the world of DevOps, a data-rich, back-office practice that presents a perfect sandbox for exploring the power of artificial intelligence. The teams in charge of operations now have a burgeoning collection of labor-saving and efficiency-boosting tools and platforms on offer under the acronym AIops, all of which promise to apply the best artificial intelligence algorithms to the work of maintaining IT infrastructure.
AIops is among the better use cases for artificial intelligence. Servers and networks generate petabytes upon petabytes of data. We know when processes start and stop, surge and ebb, often down to the millisecond. RAM and CPU demands are often well-understood and so are the prices for renting hardware in the cloud. All are often calculated down to six or seven significant digits. Creating an autonomous car may mean struggling with a world filled with pedestrians, livestock, and shadows, but when it comes to IT infrastructure, everything is already digitized and ready for analysis.
Some of the simplest tasks for AIops involve speeding up the way software is deployed to cloud instances. All the work that DevOps teams do can be enhanced with smarter automation capable of watching loads, predicting demand, and even starting up new instances when the hordes descend.
Good AIops tools generate forward-looking guesses about machine load and then watch to see if anything deviates from these estimates. Anomalies might be turned into alerts that generate emails, Slack posts, or, if the deviation is large enough, pager messages. A good part of the AIops stack is devoted to managing alerts and ensuring that only the most significant problems turn into something that interrupts a meeting or a good night’s sleep.