The real reason why AI can't predict DevOps outages | Try this

A lot of DevOps and SR engineers complain when using AI for instant management, they don't get the expected result. Now this is actually true because in terms of instant management, data is messy and data is very scattered. Let's take a very simple example. Just try asking AI what is the reason for system going down 20 minutes ago or try asking AI what is the reason for latency at 5:00 a. m. in the morning. It's very difficult for AI to get the context because some information is in the logs, metrics and some information is somewhere else and on top of that all of this information is timesensitive over the period of time the information keeps changing. So Vishek then what is the solution in this case? The best option in this case is to go with time series data. For example, time scale DB. Time scale DB is built on Postgress SQL. It combines the time series performance with relational integrity. So agents can query one system and get the complete answers. So all it takes for the AI agents is to run one join statement and it can understand what exactly happened, why it happened and where exactly it happened. And on top of that, time scale DB also supports an MCP server. So AI agents can query the MCP server and get the structural data in plain English. So especially if you're working in domains or industries like manufacturing, time scale DB is very useful because modern factories run on continuous streams of machine signals, sensor readings, alarms, production count, and even maintenance events. So in a nutshell, AI is as useful as the timebased data that it can understand. If you want to learn more about it, the creators of time scale DB, Tiger Data has put up a very interesting blog. I'll share the link in the description and also in the comment section. If you're into DevOps and cloud, I would highly encourage you to read this blog. And finally, thank you so much for watching this