Amazon SageMaker Studio for Data Scientists

This course covers the following topics:

Introduction to Amazon SageMaker Studio: An overview of SageMaker Studio’s features, including its integrated Jupyter notebooks, model debugging, and experimentation tools.

Data Preprocessing: Techniques for preparing and cleaning datasets for training and inference.

Model Building and Training: How to build and train machine learning models using SageMaker’s built-in algorithms and AutoML capabilities.

Model Deployment and Monitoring: Deploying models to SageMaker endpoints and monitoring their performance.

Continuous Integration/Continuous Deployment (CI/CD) Pipelines: Automating the process of deploying and updating models in production.