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MLOps 4: Continuous Training, Model Management, & Continuous Integration
Having discussed the data engineering side of MLOps, in the previous chapter, let’s get into model training in this chapter and how MLOps helps in making it efficient.
Managed training pipelines
We’ll start off with managed training pipelines. Similar to managed data pipelines, training pipelines play a vital role in the ML workflow.
A robust managed training pipeline helps create repeatable ML training and testing workflows while reducing human costs.
Being the core activity, as well as the most unpredictable activity in building an ML application, ML training can benefit a lot from an organized MLOps setup for the project.
What are the key training pipeline functions?
It starts with the feature store. Training inputs are fetched from the feature store for model training. The hyper parameters are also set up for training. An experiment is then planned and executed. Executing the experiment results in the ML model. The model is then…
