Describes how you can track the lineage of machine learning workflows.

Use reinforcement learning in Amazon SageMaker to solve complex machine learning problems that optimize objectives in interactive environments.

Use Amazon SageMaker Processing to process data and evaluate models with scikit-learn scripts in a Docker image provided by Amazon SageMaker.

Amazon SageMaker Studio is an integrated machine learning environment where you can build, train, deploy, and analyze your models all in the same application.

Learn how to attach and detach Git repo URLs to Amazon SageMaker Studio with this tutorial series.

To train a machine learning model, you need a large, high-quality, labeled dataset. Ground Truth helps you build high-quality training datasets for your machine learning models. With Ground Truth, you can use workers from either Amazon Mechanical Turk, a vendor company that you choose, or an internal, private workforce along with machine learning to enable you to create a labeled dataset. You can use the labeled dataset output from Ground Truth to train your own models. You can also use the output as a training dataset for an Amazon SageMaker model.

Learn more about Amazon SageMaker Model Building Pipelines.

The Amazon SageMaker Python SDK MXNet estimators and models and the Amazon SageMaker open-source MXNet container support using the MXNet deep learning framework for training and deploying models in SageMaker.

Learn more about how to get inferences from your Amazon SageMaker models and deploy your models for serving inference.

Launch instances in a placement group to cluster them logically into a low-latency group, or to spread them across hardware to reduce the risk of simultaneous failures.