The ML workflow

To develop and manage a production-ready model, you must work through the following stages:

• Source and prepare your data.
• Develop your model.

• Train an ML model on your data:

• Train model

• Evaluate model accuracy

• Tune hyperparameters

• Deploy your trained model.

• Send prediction requests to your model:

• Online prediction

• Batch prediction

• Monitor the predictions on an ongoing basis.

• Manage your models and model versions.

These stages are iterative. You may need to reevaluate and go back to a previous step at any point in the process.