# Get Started with Amazon SageMaker The next generation of Amazon SageMaker is the center for all your data, analytics, and AI. ## Overview of Amazon SageMaker Bringing together widely adopted AWS artificial intelligence and machine learning (AI/ML) and analytics capabilities, the next generation of [Amazon SageMaker](https://aws.amazon.com/sagemaker/) delivers an integrated experience for analytics and AI with unified access to all your data. The next generation of Amazon SageMaker consists of two primary components: - Amazon SageMaker Unified Studio, which provides an integrated experience to use all your data and tools for analytics and AI - Amazon SageMaker Catalog, which enables secure discovery and access to approved data and models. Additionally, SageMaker is built upon an open lakehouse architecture that unifies access to all your data across Amazon Simple Storage Service ([Amazon S3](/AmazonS3/latest/userguide/GetStartedWithS3.html)) data lakes, [Amazon Redshift](/redshift/) data warehouses, and other external sources. **Benefits** - Build with all your tools for analytics and AI in SageMaker Unified Studio - Develop and scale AI with a comprehensive set of AI capabilities - Reduce data silos and unify all your data with SageMaker lakehouse architecture - Meet your enterprise security needs with end-to-end data and AI governance To get started, go to the [Amazon SageMaker user guide](/next-generation-sagemaker/latest/userguide/what-is-sagemaker.html). ![SageMaker diagram](/images/next-generation-sagemaker/images/What_is_SageMaker_Diagram.png) ## Capabilities of Amazon SageMaker Unified Studio Amazon SageMaker Unified Studio is a single data and AI development environment where you can find and access all of the data in your organization and act on it using the best tools across any use case. - Streamline access to familiar tools and functionality from purpose-built AWS analytics and artificial intelligence and machine learning (AI/ML) services like Amazon EMR, AWS Glue, Amazon Athena, Amazon Redshift, Amazon Bedrock, and Amazon SageMaker AI. - Develop ML and foundation models (FMs) using the fully managed infrastructure, tools, and workflows of SageMaker AI. - Efficiently build generative AI applications in a trusted and secure environment using Amazon Bedrock. - Analyze, prepare, and integrate data for analytics and AI using open source frameworks on Amazon Athena, Amazon EMR, and AWS Glue - Streamline your data journey with Amazon Q Developer to author code, generate SQL, integrate data, and troubleshoot. ## Key concepts To learn more about Amazon SageMaker Unified Studio, Catalog, lakehouse architecture, and SageMaker AI, explore the following guides: - **SageMaker Unified Studio**: Build with all your data and tools for analytics and AI in a single development environment with SageMaker Unified Studio [Learn more](/sagemaker-unified-studio/latest/userguide/what-is-sagemaker-unified-studio.html) - **SageMaker lakehouse architecture**: Unify data access across Amazon S3 data lakes, Amazon Redshift data warehouses, and third-party and federated data sources with SageMaker lakehouse architecture [Learn more](/sagemaker-lakehouse-architecture/latest/userguide/what-is-smlh.html) - **SageMaker Catalog**: Securely discover, govern, and collaborate on data and AI with SageMaker Catalog (built on Amazon DataZone) [Learn more](/datazone/latest/userguide/what-is-datazone.html) - **Amazon SageMaker AI**: Build, train, and deploy ML models—including FMs—for any use case with fully managed infrastructure, tools, and workflows. [Learn more](/sagemaker/latest/dg/whatis.html) ## Use cases Learn more about how to apply your use case to SageMaker Unified Studio and which underlying services are being used. ### Import and query data You can use the SageMaker Unified Studio query editor to perform analysis using SQL. It provides a place to write and run queries, view results, and share your work with your team. You can also import and query data sets in your existing Glue Data Catalog resources. [Amazon Athena](/athena) [AWS Glue](/glue) ### Scale data infrastructure and integrate access controls SageMaker lakehouse architecture provides a unified environment for accessing, discovering, preparing, and analyzing data from various sources for machine learning (ML) and analytics workloads. Use lakehouse access controls to: - Streamlining the creation of connections to diverse data sources through a unified interface - Centralizing access control management through [AWS Lake Formation](https://aws.amazon.com/lake-formation/) - Enabling in-place querying through federated catalogs without data movement - Providing fine-grained permissions at the catalog, database, table, and column levels - Exploring data for ad hoc reporting and proof of concept before setting up new zero-ETL pipelines [AWS Lake Formation](/lake-formation) ### Fine-tune foundation models Amazon SageMaker Unified Studio provides a large collection of state-of-the-art foundation models. These models support use cases such as content writing, code generation, question answering, copywriting, summarization, classification, information retrieval, and more. You can find, customize, and deploy these foundation models in the JumpStart model catalog. You can use the foundation models to build your own generative AI solutions for a wide range of applications. [Amazon SageMaker AI](/sagemaker) ### Create a chat agent application Amazon Bedrock in SageMaker Unified Studio offers multiple playgrounds that allow you to easily access and experiment with Amazon Bedrock models. With the [chat](/sagemaker-unified-studio/latest/userguide/bedrock-explore-chat-playground.html) playground, you can chat with a model through text and image prompts. With the [image and video](/sagemaker-unified-studio/latest/userguide/explore-image-playground.html) playground, you can use a compatible model to generate and edit images and videos. In addition to the playgrounds, you can also use Amazon Bedrock in SageMaker Unified Studio to create [chat agent apps](/sagemaker-unified-studio/latest/userguide/create-chat-app.html) and [flows apps](/sagemaker-unified-studio/latest/userguide/create-flows-app.html). [Amazon Bedrock](/bedrock) ## Start building Now that we've covered what Amazon SageMaker is and its benefits, you can get started by selecting one of the following workflows: ### Setup **[Setting up Amazon SageMaker](/next-generation-sagemaker/latest/userguide/setting-up.html)** Learn how to set up Amazon SageMaker. ### Upload and query data **[Get started with uploading and querying data](/next-generation-sagemaker/latest/userguide/upload-query.html)** Learn how to write and run queries, view results, and share your work with your team. ### Bring existing resources **[Get started with importing and querying data sets for AWS Glue Data Catalog and Amazon S3 in Amazon SageMaker Unified Studio](/next-generation-sagemaker/latest/userguide/getting-started-sagemaker-gdc-s3.html)** Learn how to access and leverage your existing AWS Glue Data Catalog resources within Amazon SageMaker Unified Studio, allowing you to query and analyze your data without moving or duplicating it. ### Get started with compute **[Get started using EMR Serverless in Amazon SageMaker Unified Studio](/next-generation-sagemaker/latest/userguide/emr-serverless.html)** Use a single EMR Serverless application on multiple clusters and run clusters on demand as it fits your use case and needs. ### Model Development **[Get started fine-tuning foundation models](/next-generation-sagemaker/latest/userguide/getting-started-sagemaker-training.html)** Learn how to fine-tune foundation models, Amazon SageMaker Unified Studio provides an example training dataset for each model that's eligible for training. ### Get started with lakehouse architecture **[Get started with lakehouse access controls for Athena federated queries in Amazon SageMaker Unified Studio](/next-generation-sagemaker/latest/userguide/lakehouse-athena-federated-queries.html)** This guide shows you how to use SageMaker lakehouse architecture with integrated access controls for Athena federated queries. ## Resources - **Learn**: Public foundation models ([Anthropic Claude](https://aws.amazon.com/bedrock/claude/), [Cohere Command Embed](https://aws.amazon.com/bedrock/cohere-command-embed/), [AI21 Labs Jurassic](https://aws.amazon.com/bedrock/jurassic/),[Meta Llama](https://aws.amazon.com/bedrock/llama/), [Mistral AI](https://aws.amazon.com/bedrock/mistral/), [Stable Diffusion XL](https://aws.amazon.com/bedrock/stable-diffusion/), [Amazon Nova](https://aws.amazon.com/ai/generative-ai/nova/),[Amazon Titan](https://aws.amazon.com/bedrock/titan/)) [PartyRock, an Amazon Bedrock Playground](https://partyrock.aws/) for hands-on prompt engineering experimentation - **Build**: [Build private and secure enterprise generative AI apps with Amazon Q Business and IAM Identity Center](https://aws.amazon.com/blogs/machine-learning/build-private-and-secure-enterprise-generative-ai-apps-with-amazon-q-business-and-aws-iam-identity-center/) [Fine-tune and deploy language models with Amazon SageMaker AI](https://aws.amazon.com/blogs/machine-learning/fine-tune-and-deploy-language-models-with-amazon-sagemaker-canvas-and-amazon-bedrock/) [Build generative AI applications with Amazon Bedrock Studio](https://aws.amazon.com/blogs/aws/build-generative-ai-applications-with-amazon-bedrock-studio-preview/) [Build enterprise-grade applications with natural language using AWS App Studio (preview)](https://aws.amazon.com/blogs/aws/build-custom-business-applications-without-cloud-expertise-using-aws-app-studio-preview/) - **Discover**: [Amazon Q capabilities to reimagine developer experience](https://aws.amazon.com/blogs/aws/amazon-q-developer-now-generally-available-includes-new-capabilities-to-reimagine-developer-experience/) [Chat about your AWS account resources with Amazon Q](https://aws.amazon.com/blogs/devops/chat-about-your-aws-account-resources-with-amazon-q-developer/) [Amazon Bedrock model evaluation is now generally available](https://aws.amazon.com/blogs/aws/amazon-bedrock-model-evaluation-is-now-generally-available/)