What happens with models after they are trained? "Whatever they were meant for," you would say, and you would be completely right. However, what also awaits around the corner are tons of questions like: How to deploy? Where to deploy? How to scale? And, of course, how to limit the budget to a reasonable paycheck?

And here it comes: K8s could save our lives again, and in this session, we will explore 3 and 1 additional K8s-based tools to manage your MLs effectively! All of them we will test against a custom LLM model for performance, scalability and user-friendly deploy process of course!