ML

A practical, connected roadmap from Python and ML foundations to LLMs, RAG, agents, evaluation, production AI, and cloud architecture.

Legacy SageMaker notebook workflow for building, training, and deploying models. Verify current AWS service and SDK guidance before production use.

Learn how to create a Jupyter notebook in the SageMaker notebook instance

Learn how to create a Jupyter notebook in the SageMaker notebook instance

Learn how to Split the Dataset into Train, Validation, and Test Datasets

Learn how to Convert the Dataset into CSV Files

Learn how to upload the Dataset to Amazon S3

Learn how to How to Train the Model

Learn how to How to Deploy the Model

Learn how to How to Evaluate the Model

Legacy SageMaker notebook workflow for building, training, deploying, and evaluating an XGBoost model. Verify current SDK APIs before production use.

Learn How to a simple text generator with Amazon Bedrock, LangChain, and Streamlit.

Understand the current Amazon SageMaker AI naming, platform structure, notebook options, and production ML workflow in 2026.

How embeddings represent data as vectors and how similarity search supports retrieval systems.

Understand Amazon SageMaker AI for building, training, and deploying machine-learning models.

A production-oriented view of problem framing, data, training, evaluation, deployment, monitoring, and iteration.

A reusable architecture case for training, evaluating, deploying, monitoring, and safely updating machine-learning models.

Learn how to learn Python fast.

Learn How to learn generative AI easily.

Learn about AI from scratch.

A structured collection of observations, examples, records, or other data used for analysis, training, validation, or evaluation.

Demand for the cloud has been boosted since the beginning of the pandemic, as businesses try to build resiliency.

Learn about World’s most popular Python/R distribution

Learn about AI from scratch.

First Service You should learn in AWS.

Massive data when grouped into a collection called a dataset.

Massive data when grouped into a collection called a dataset.

Massive data when grouped into a collection called a dataset.

Understand Amazon SageMaker notebook instances and how they relate to Jupyter-based ML development.

Popular and powerful machine learning algorithm that falls under the category of gradient boosting.