Key features

Connect to data sources, import data, verify data quality, engineer model features using 300+ built-in data transformations, and save them to SageMaker Feature Store in few simple clicks using SageMaker Data Wrangler within Studio.

Create, browse, and connect to Amazon EMR clusters directly from SageMaker Studio Notebooks. You can even debug your Spark jobs using familiar tools such as Spark UI right from the notebook.

Connect to data stores, spin up the resources to run your data processing job, save the output to persistent storage, and provide logs and metrics using SageMaker Processing within Studio.

Store, share, and manage ML model features for training and inference to promote feature reuse across ML applications using SageMaker Feature Store, a fully managed, purpose-built repository, in Studio. You get the same features consistently both during training and during inference, saving months of development effort.

Access to fully managed Jupyter Notebooks in Studio in one click. SageMaker Studio Notebooks come pre-configured with deep learning environments for AWS-optimized TensorFlow and PyTorch to help you quickly get started with model building. You can easily dial up or down the underlying compute resources without interrupting your work.

Share the SageMaker Studio Notebook with a single click, and your colleagues get the same notebook that is easy to replicate with all its output.

Use over 15 built-in algorithms available in pre-built container images that can be used to quickly train and run inference or bring your own custom images to Studio.

Automatically build, train, and tune the best ML models, based on your data while maintaining full control and visibility using SageMaker Autopilot in Studio. You then directly deploy model to production with just one click or use generated notebooks to improve model quality.

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

Set up a distributed compute cluster, perform the training, output results to Amazon S3, and tear down the cluster in a single click. Train models at scale using SageMaker data parallel and model parallel libraries, and accelerate training process by up to 50% through graph- and kernel-level optimizations by using SageMaker Training Compiler, within Studio. You can reduce costs by up to 90% by using managed spot instance training.

Track iterations to ML models by capturing the input parameters, configurations, and results, and storing them as experiments using SageMaker Experiments within Studio. You can browse active experiments, search for previous experiments, review previous experiments with their results, and compare experiment results.

Automatically tune your model by adjusting thousands of combinations of algorithm parameters to arrive at the most accurate predictions the model is capable of producing, saving weeks of effort.

Capture metrics and profiles training jobs in real-time so you can correct performance problems quickly before the model is deployed to production using SageMaker Debugger within Studio.

Deploy your trained model into production with a single click. Access to SageMaker Model Deployment within Studio for all your inference needs, from low latency (a few milliseconds) and high throughput (hundreds of thousands of requests per second) to long-running inference for use cases such as natural language processing and computer vision.

Deploy thousands of models on a single endpoint using SageMaker’s multi-model endpoints and multi-container endpoints within Stuio, improving cost-effectiveness while providing the flexibility to use models as often as you need them.

Track model versions, their metadata, and performance sing SageMaker Model Registry within Studio, making it easy to choose the right model for deployment based on your business requirements. In addition, you can automatically log approval workflows for audit and compliance.

Bring CI/CD practices to ML using SageMaker Projects within Studio, such as maintaining parity between development and production environments, source and version control, A/B testing, and end-to-end automation.

Maintain quality by detecting model drift and concept drift in real time using SageMaker Model Monitor within Studio. All models trained in SageMaker automatically emit key metrics that can be collected and viewed in Studio.

Automate the entire model build workflow, including data preparation, feature engineering, model training, model tuning, and model validation using SageMaker Pipelines in Studio. You can configure SageMaker Pipelines to run automatically at regular intervals or when certain events are triggered, or run them manually as needed.