MLOps is a collection of best practices for putting machine learning models into production and then maintaining those models in production. In this comprehensive data demo, you’ll learn how MLOps spans the machine learning lifecycle from data preparation, through model training and tuning, and improving deployed models.
This repo will guide you through how to use Python’s MLflow package to track model experiments and set up models to deploy into production.
Key Takeaways:
- Learn the basics of MLOps and its capabilities
- Create machine learning experiments in Python’s MLflow
- Package trained machine learning model for production with MLflow