Defining the MLOps Stack
MLOps has quickly become one of the buzzwords in machine learning and artificial intelligence. And we get it - it’s all about how you can create a smooth and repeatable machine learning workflow, which is a good thing.
But it’s time to get passed the hype and have an open conversation about MLOps, what it means to you as a data scientist, and what it takes to actually implement MLOps in production.
In this on-demand panel, panelists from Pachyderm, Mona Labs, and Cortex Labs discuss:
- How they define MLOps, and if/why you should care
- The key processes, workflows, and tech stack that you need to achieve across the entire MLOps lifecycle
- Examples of critical pitfalls and hard lessons learned with machine learning processes and workflows
On-Demand Panel:
