Integrated development environment (IDE) for the ML lifecycle

With SageMaker Data Wrangler’s data selection tool, you can quickly select data from multiple data sources, such as Amazon Athena, Amazon Redshift, AWS Lake Formation, Amazon S3, and the Amazon SageMaker Feature Store. You can write queries for data sources and import data directly into SageMaker from various file formats, and use SageMaker Data Wrangler’s visualization templates and built-in data transforms to ensure data prepared will result in accurate ML models.

A fully managed repository to store, update, retrieve, and share machine learning features, SageMaker Feature Store serves the exact same features in batch for training and in real-time for inference so you don’t need to write code to keep features consistent. You can easily add new features, update existing ones, retrieve features in batches for training, and get the same features with single-digit millisecond latency for real-time inference.

You can visually browse, discover, and connect to Apache Spark data processing environments running on Amazon EMR from your SageMaker Studio notebooks with a few clicks. Once connected, you can interactively query, explore, and visualize data, and run Spark jobs using the language of your choice (SQL, Python, and Scala) to build end-to-end data preparation and ML workflows.

Amazon SageMaker data labeling allows you to identify raw data, such as images, text files, and videos, and add informative labels to create high-quality training datasets for your machine learning models.

Amazon SageMaker Studio Notebooks are one-click Jupyter Notebooks that can be spun up quickly. The underlying compute resources are fully elastic, so you can easily dial up or down the available resources and the changes take place automatically in the background without interrupting your work. Notebooks can be shared with a single click, your colleagues get the exact same notebook, saved in the same place.

Amazon SageMaker offers over 15 built-in algorithms available in pre-built container images that can be used to quickly train and run inference.

Amazon SageMaker JumpStart helps you quickly get started with ML using pre-built solutions that can be deployed with just a few clicks. SageMaker JumpStart also supports one-click deployment and fine-tuning of more than 150 popular open-source models.

Amazon SageMaker is optimized for many popular deep learning frameworks such as TensorFlow, Apache MXNet, PyTorch, and more. Frameworks are always up-to-date with the latest version, and are optimized for performance on AWS. You don’t need to manually setup these frameworks and can use them within the built-in containers.

Amazon SageMaker Clarify provides data to improve model quality through bias detection during data preparation and after training. SageMaker Clarify also provides model explainability reports so stakeholders can see how and why models make predictions.

Amazon SageMaker Experiments automatically captures training input parameters, configurations, and results, and stores them as ‘experiments’. You can browse active experiments, search for previous experiments by their characteristics, review previous experiments with their results, and compare experiment results visually.

Amazon SageMaker Debugger captures metrics in real-time so you can correct performance problems quickly before the model is deployed to production.

Amazon SageMaker Model Monitor automatically detects model and concept drifts and provides detailed alerts that help identify the source of the problem so you can improve model quality over time. All models trained in SageMaker automatically emit key metrics that can be collected and viewed in SageMaker Studio.

Amazon SageMaker provides the broadest selection of machine learning (ML) infrastructure and model deployment options meeting the needs of your use case, whether real-time or batch, so you can easily deploy your ML models at scale. SageMaker supports the entire spectrum of inference requirements ranging from low latency (a few milliseconds) and high throughput (hundreds of thousands of inference requests per second), to long-running inference for use cases such as natural language processing (NLP) and computer vision (CV).