Ok, we’ve established that your ML team needs some basic tooling in order to be effective, and that that tooling needs to provide support for various aspects of the machine learning workflow, from data acquisition and management, to model development and optimization, to model deployment and monitoring.
But how do you get there? It’s 2022 and we’re not living in the dark ages anymore. There are lots of tools available off the shelf, both commercial and open source, that can help.
At the extremes, tools fall into one of a couple of buckets. End-to-end platforms that try to provide support for lots of different aspects of the ML lifecycle, and specialized tools that offer deep functionality in a particular domain or area.
Our panelists will debate the merits of these approaches in this session, The Great MLOps Debate: End-to-End ML Platforms vs Specialized Tools.
