This video and post are part of a One Dev Question series on MLOps - DevOps for Machine Learning. See the full video playlist here, and the rest of the blog posts here.
Another term you may have heard in this space is "AIOps". It would be easy to assume it was another word for the same thing, but that's not the case.
MLOps and AIOps both sit at the union of DevOps and AI. They may sound like the same thing, but they represent completely different ideas. So what is AIOps?
AI Ops, or "Artificial Intelligence for IT Operations" is the reverse of MLOps in one respect - it's the application of ML to DevOps, rather than the application of DevOps to ML.
AIOps is often described as artificial intelligence applied to IT operations, implying that it's limited to the day to day management of configuration, infrastructure, networking, and other IT operations aspects. But applied AIOps can also involve automated continuous delivery of change to those systems.
Some examples of applications of AIOps include:
- You might have a predictive model to analyse a set of code changes to determine the probability that this change will introduce an unexpected issue so you can give them an extra look before going live. You could base that prediction on all your previous releases and their success or failure.
- You could use anomaly detection over your network traffic and automatically modify firewall rules to mitigate unusual behaviour.
- You could use a recommendation engine to identify related log entries across multiple sources of data to help identify and remediate a complex issue.
When two broad fields collide (DevOps/IT Operations and Machine Learning), it opens up a world of possibilities. Machine Learning can be used to help solve those tricky problems where a solution isn't obvious, and there are plenty of those in DevOps!