Tori Tompkins explains what MLOps is and why it’s valuable:
A ML project will typically begin in an ‘Explore Phase’ where a data scientist or team of data scientists will explore the data they currently have and experiment with models, algorithms, parameters and features. MLOps at this stage is responsible for supplying Data Scientists with environment they need to achieve this. One way this can be done is by leveraging Feature Store.
A feature store is a tool for storing commonly used features. As data scientists create new features then can log these into feature stores such as Feast and Databricks Feature Store, they can reuse these features across teams and projects. This will benefit teams in multiple ways by reducing compute times for both training and inference, provide consistency in common features and reducing effort for create complex logic.
Read on for information about all six phases.