Step 1: Measure the performance of your model by using relevant metrics that assess the model. For regression models, use Adjusted R-squared to measure the performance of the model against that of a benchmark. For classification, use the AUC (Area Under the Curve) of a ROC curve (Receiver Operating Characteristics).
Step 2: Validate the model by monitoring its Bias error, Variance error, Model Fit, and Model Dimensions. Use Cross Validation to check for bias.
Step 3: Evaluate the model using historical data (offline) or live data. If using historical data, use a Jupyter notebook and either the AWS SDK for Python (Boto) or the high-level Python library provided by SageMaker. If using live data, use SageMaker’s A/B testing for models in production and deploy production variants.
Step 4: Compare the results using the relevant metrics and determine whether the model’s performance and accuracy enable you to achieve your business goals.