More talks in the program:
Machine Learning is hot but organisations are struggling to run it in live and MLOps is not easy to master. DevOps skills are needed but in more than just the usual DevOps ways. The key reasons are that the development/delivery workflow is different and the kind of software artifacts involved are different. We will explore the differences and look at emerging open source projects in order to appreciate why the DevOps for machine learning space is growing and the needs that it addresses.
Getting a better picture of MLOps will help us in lots of ways. It will help us evaluate Machine Learning Operations (MLOps) tools and platforms. We’ll come to understand the specific needs for governance, versioning and monitoring in machine learning. We’ll get a sense for the range of MLOps use-cases and better understand how to scope Machine Learning projects.