This repository is a collection of Jupyter notebooks providing reference implementations and templates for building, training, and deploying machine learning models using Amazon SageMaker. It serves as an example library for implementing model architectures and automating the machine learning lifecycle.
The library provides practical patterns for machine learning training, data engineering, and model deployment. It includes implementation guides for MLOps, including workflows for model monitoring, lineage tracking, and hyperparameter tuning.
The examples cover a broad range of capabilities including automated model selection, large language model fine-tuning, and reinforcement learning. It also demonstrates data engineering tasks such as dataset labeling and feature store management, as well as inference strategies like real-time endpoints and batch transform jobs.
These examples are delivered as interactive notebooks designed for exploratory data analysis and iterative workflow development.