Amazon SageMaker Modeling

In the previous chapter, you learned several methods of model optimization and evaluation techniques. You also learned various ways of storing data, processing data, and applying different statistical approaches to data. So, how can you now build a pipeline for this? Well, you can read data, process data, and build machine learning (ML) models on the processed data. But what if my first ML model does not perform well? Can I fine-tune my model? The answer is yes; you can do nearly everything using Amazon SageMaker. In this chapter, you will walk you through the following topics using Amazon SageMaker:

• Understanding different instances of Amazon SageMaker
• Cleaning and preparing data in Jupyter Notebook in Amazon SageMaker
• Model training in Amazon SageMaker
• Using SageMaker’s built-in ML algorithms
• Writing custom training and inference code in SageMaker