In this course,
First, you will learn how training-serving skew, concept drift, and overfitting are different causes of model underperformance, and how they can be mitigated by post-deployment monitoring.
Next, you will discover how ML models can be deployed, that is made available on HTTP endpoints, using Flask, the popular Python web-serving framework. You will also see how you can deploy models to serverless environments such as Google Cloud Functions
Finally, you will work with platform-specific machine learning services such as Google AI Platform and Amazon SageMaker for model deployment.