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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.
To be Decided
We recommend students who are not experienced data scientists complete the following two courses followed by 1-year on-the-job experience building models prior to taking this course:
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