Build, train, and deploy ML models with Amazon SageMaker AI (formerly Amazon SageMaker)

Amazon SageMaker

The next generation of Amazon SageMaker is the center for all your data, analytics, and AI
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Overview

Bringing together widely adopted AWS machine learning (ML) and analytics capabilities, Amazon SageMaker delivers an integrated experience for analytics and AI with unified access to all your data. Collaborate and build faster from a unified studio (preview) using familiar AWS tools for model development, generative AI, data processing, and SQL analytics, accelerated by Amazon Q Developer, the most capable generative AI assistant for software development. Access all your data whether it’s stored in data lakes, data warehouses, or third-party or federated data sources, with governance built in to meet enterprise security needs.

Benefits

Amazon SageMaker Unified Studio (preview) provides an integrated experience to use all your data and tools for analytics and AI. Discover your data and put it to work using familiar AWS tools for model development, generative AI, data processing, and SQL analytics. Work across compute resources using unified notebooks, discover and query diverse data sources with a built-in SQL editor, train and deploy AI models at scale, and rapidly build custom generative AI applications. Create and securely share analytics and AI artifacts such as data, models, and generative AI applications to bring data products to market faster.
Accelerate AI in SageMaker with a comprehensive set of AI development capabilities that are secure by design. Train, customize, and deploy ML and foundation models (FMs) on a highly performant and cost-effective infrastructure. Use purpose-built tools spanning the entire AI lifecycle— from high-performance integrated development environments (IDEs) and distributed training to inference, AI ops, governance, and observability. Rapidly create generative AI applications tailored to your business with cutting-edge models and your proprietary data. Speed up AI development with Amazon Q Developer, helping you more easily discover data, build and train ML models, generate SQL queries, and create and run data pipeline jobs, all through natural language.
Unify all your data across Amazon Simple Storage Service (Amazon S3) data lakes and Amazon Redshift data warehouses with Amazon SageMaker Lakehouse. Gain the flexibility to access and query your data with all Apache Iceberg–compatible tools and engines on a single copy of analytics data. Secure your data by defining fine-grained permissions, applied across your analytics and AI tools in the lakehouse. Bring data from operational databases and applications into your lakehouse in near real time through zero-ETL integrations. Additionally, access and query data in place with federated query capabilities across third-party data sources.
Ensure enterprise security with built-in governance throughout the entire data and AI lifecycle. SageMaker empowers you to control access to the right data, models, and development artifacts by the right user for the right purpose. Consistently define and enforce access policies using a single permission model with fine-grained access controls with Amazon SageMaker Catalog. Safeguard and protect your AI models with data classification, toxicity detection, guardrails, and responsible AI policies. Gain trust throughout your organization through data-quality monitoring and automation, sensitive data detection, and data and ML lineage.

Meet the next generation of SageMaker

Capabilities

Customers

Toyota

“To address siloed data sets spread across our automotive operations, we are exploring Amazon SageMaker to unify and govern data across our connected car, sales, manufacturing, and supply chain units. This approach allows us to search, discover, and share data effortlessly, laying the groundwork to pre-empt quality issues, increase customer safety and satisfaction, and accelerate development of generative AI applications.”

Kamal Distell, VP of Data, Analytics, Platforms, and Data Science, TMNA

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NatWest Group

"Our Data Platform Engineering team has been deploying multiple end-user tools for data engineering, ML, SQL, and gen AI tasks. As we look to simplify processes across the bank, we’ve been looking at streamlining user authentication and data access authorization. Amazon SageMaker delivers a ready-made user experience to help us deploy one single environment across the organization, reducing the time required for our data users to access new tools by around 50%."

Zachery Anderson, CDAO, NatWest Group

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Roche

"We have been using Amazon Redshift to gain insights from both structured and semistructured data across all our data repositories. The new Amazon SageMaker Lakehouse excites me with its potential to enhance and unify access to data lake, and other data sources with services like Amazon Redshift, Glue Data Catalog, and Lake Formation. This innovation will allow our data and engineering teams to simplify data access, promoting interoperability across data, analytics, and application workloads. I foresee a notable reduction in data errors through less data copying, a 40% decrease in processing time, quicker analytics data write-back to transactional systems for improved decision-making, and empowering our teams to focus on creating business value.”

Yannick Misteli, Head of Engineering, Global Product Strategy, Roche

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Lennar

“We have spent the last 18 months working with AWS to transform our data foundation to use best-in-class solutions that are cost-effective as well. With advancements like Amazon SageMaker Unified Studio and Amazon SageMaker Lakehouse, we expect to accelerate our velocity of delivery through seamless access to data and services, thus enabling our engineers, analysts, and scientists to surface insights that provide material value to our business.”

Lee Slezak, SVP of Data and Analytic, Lennar

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Natera, Inc

"Our organization has been leveraging Amazon DataZone, Amazon SageMaker AI, Amazon Athena, and Amazon Redshift to manage and analyze our clinical and genomic data. We are excited to now have the unified governance of the Amazon SageMaker Catalog, which will streamline our data discovery and access, enabling our team to quickly analyze relevant data across our whole domain. This integration will help us create tailored datasets, potentially reducing our time-to-insight, and ultimately drive improved patient outcomes as we advance our goal of making personalized genetic testing a standard part of care."

Mirko Buholzer, VP of Software Engineering, Natera, Inc.

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