Hands-On Automated Machine Learning

Deployment

This is the phase where you start to operationalize your ML models.

In this phase, you are typically trying to address the following items:

• Having a short test drive in production
• Monitoring the performance
• Identifying the strategy to improve the model such as re-evaluation, a retraining loop, and a redeployment loop, when model performance starts to decay
• Preparing the final report and deliverables

You can definitely extend the list of items that you are going to address for each phase, but this list should give you an overall idea of what you need to cover.

There will be numerous times when you will be joining a project team in the middle of a project's timeline. In these kinds of situations, you usually lack the project background ...