One of my coworkers used basic reinforcement learning to automate a task someone used to have to do manually. We have two data ingestion pipelines. One that we ingest immediately, and a second for our larger customers which is throttled during the day and ingested at night. For the throttled pipeline, we initially had hard coded rate limits, but as we made changes to our infrastructure, the throttle was processing a different amount than it should have been. Sometimes it would process too much, and we would start to see latency build up in our normal pipeline, and other times it processed too little. For a short period of time, we had the hard coded throttle with a Slack command to override the default. This allowed an enginneer to change the rate limit if they saw we were ingesting to little or too much. While this worked, it was common that an engineer wasn't paying attention, and we would process the wrong amount for a period of time. One of my coworkers used extremely basic reinforcement learning to make the throttle dynamic. It looks at the latency of the normal ingestion pipeline, and based on that, decides how high to set the rate limit on the throttled pipeline. Thanks to him, the throttle will automatically process as much as it can, and no one needs to watch it.

The same coworker also used decision trees to analyze query performance. He trained a decision tree on the words contained in the raw SQL query and the query plan. Anyone could then read the decision tree to understand what properties of a query made that query slow. There's been times we're we've noticed some queries having odd behavior going on, such as some queries having unusually high planning time. When something like this happens, we are able to train a decision tree based on the odd behavior we've noticed. We can then read the decision tree to see what queries have the weird behavior.