KubeCon + CloudNativeCon India 2024

As AI becomes more popular, it's bringing some new headaches when it comes to managing models, from building them to getting them into production. The usual DevOps tools don't always cut it, especially when you need everything to be consistent and handle large datasets without a hitch. As ML projects grow, trying to manage everything manually just gets messy and prone to mistakes. In this talk, I'll break down how GitOps can make life easier by automating and simplifying ML operations. With Git as the go-to source for everything, GitOps keeps your ML models consistent and reproducible across different environments. I'll share some real-world examples, talk about the challenges, and give you practical tips on how to bring GitOps into your ML workflow. The goal is to help you deploy models faster and with fewer headaches.