Introduction
Machine learning operations (MLOps) aims to more efficiently and easily scale from an experimental project to a machine learning workload in production.
To train a model, you'll want to experiment with many different configurations in an easy-to-use environment. On the other hand, to deploy a model to production, you want a set-up that is ready to scale and future-proof.
As machine learning often requires both an experimentation or development environment and a production environment, you'll want to use continuous delivery to automate the process of moving a model from development to production.
Learning objectives
In this module you'll learn:
- How to set up environments for development and production.
- How to control deployments with approval gates.