Amazon S3Used for storing and retrieving large datasets, model artifacts, and other data needed for training and inference.
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AWS Identity and Access Management (IAM)Provides fine-grained access control policies to manage permissions for SageMaker resources and actions.
AWS LambdaEnables integration with SageMaker endpoints for real-time data processing and event-driven workflows.
Amazon CloudWatchProvides monitoring and logging for SageMaker resources, allowing you to track metrics and set alarms.
AWS GlueOffers data preparation and transformation capabilities to preprocess data before feeding it into SageMaker models.
Amazon RedshiftAllows you to perform advanced analytics and store large amounts of structured data, which can be used in SageMaker.
AWS Data PipelineEnables data workflow automation to transport data between various AWS services and SageMaker.
AWS Step FunctionsFacilitates building and orchestrating machine learning workflow pipelines using SageMaker.
Amazon API GatewayAPI Gateway can expose SageMaker inference endpoints through a REST API using an AWS service integration. This adds API authentication, throttling, and request mapping in front of model inference.
Amazon AthenaSageMaker notebooks, Data Wrangler, and Unified Studio can query data with Athena for analysis and machine learning preparation. Studio also supports propagating users' identities to Athena for governed data access.
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Amazon Augmented AI (Amazon A2I)SageMaker inference workflows can use Amazon A2I to send selected predictions to human reviewers. A custom human-review workflow can, for example, route low-confidence model results to a review workforce.
Amazon AuroraSageMaker Lakehouse can receive operational data through Aurora zero-ETL integrations. Aurora Machine Learning also lets SQL queries invoke SageMaker endpoints, such as scoring customer records without moving inference logic into an application.
Amazon Aurora ServerlessSupported Aurora Serverless v2 configurations can use Aurora Machine Learning to invoke SageMaker endpoints from SQL. Supported Serverless v2 databases can also supply data to SageMaker Lakehouse through Aurora zero-ETL integrations.
Amazon Bedrock
SageMaker Unified Studio integrates Bedrock tools for developing generative AI applications alongside analytics and machine learning workflows. Supported models trained or fine-tuned in SageMaker can also be imported into Bedrock through Custom Model Import.
Amazon CodeWhispererSageMaker Studio integrated CodeWhisperer code suggestions into its notebook development environment. These capabilities are now part of Amazon Q Developer.
Amazon CognitoSageMaker Ground Truth can use Cognito user pools to authenticate members of private labeling workforces. Workers sign in to the labeling portal with identities managed in the associated user pool.
Amazon ComprehendSageMaker Canvas provides ready-to-use text analysis models powered by Comprehend. Users can perform tasks such as sentiment analysis, entity extraction, and personally identifiable information detection without training a custom model.
Amazon DataZone
SageMaker Unified Studio uses DataZone capabilities for domains, projects, and governed data collaboration. SageMaker Catalog builds on DataZone to help users discover data assets and request access for analytics and AI work.
Amazon DynamoDBSageMaker Lakehouse can receive DynamoDB data through zero-ETL integration capabilities in AWS Glue. This makes operational table data available for analytics and machine learning without maintaining a custom extraction pipeline.
Amazon EC2SageMaker provisions and manages EC2-backed compute for notebooks, training, processing, and model hosting. Training jobs can also use managed Spot Training to run on spare EC2 capacity with interruption handling.
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Amazon Elastic Block Store (Amazon EBS)SageMaker uses EBS volumes for notebook storage and for storage attached to supported training, processing, and inference instances. Volume size and encryption settings can be configured for the relevant SageMaker resources.
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Amazon Elastic Container Registry (Amazon ECR)SageMaker pulls training, processing, and inference container images from ECR. Custom algorithms and model-serving environments can be packaged as images in private ECR repositories.
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Amazon Elastic File System (Amazon EFS)SageMaker Studio can use EFS for persistent user files, and supported training jobs can read training data directly from EFS. This allows notebooks or training jobs to work with a shared file system instead of copying every dataset into local storage.
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Amazon Elastic Kubernetes Service (Amazon EKS)SageMaker HyperPod supports EKS as an orchestrator for resilient machine learning clusters. SageMaker Operators for Kubernetes also let Kubernetes workloads create and manage SageMaker jobs and inference resources.
Amazon EMRSageMaker Studio notebooks can connect to EMR clusters and EMR Serverless applications to run Spark data processing. Supported connections can propagate the user's identity for governed access to data.
Amazon EventBridgeSageMaker publishes job, pipeline, endpoint, and other resource state-change events to EventBridge. EventBridge rules and schedules can also start SageMaker pipelines, enabling event-driven retraining and deployment workflows.
Amazon ForecastSageMaker Canvas has integrated Amazon Forecast technology for building time-series forecasting models through its no-code interface. This supports workflows such as predicting product demand from historical sales data.
Amazon Fraud DetectorModels hosted on SageMaker endpoints can be imported into Fraud Detector and used in fraud detection workflows. Their prediction scores can be combined with detector rules and other fraud signals.
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Amazon FSxSageMaker training jobs can use FSx for Lustre file systems as training data sources. This provides high-throughput shared file access for large datasets, including datasets linked to S3.
Amazon Kinesis Video StreamsAWS provides an inference integration template that reads video from Kinesis Video Streams and sends frames to a SageMaker endpoint. This supports applications such as detecting objects in live camera footage.
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Amazon Managed Workflows for Apache Airflow (Amazon MWAA)MWAA can orchestrate SageMaker through Airflow's SageMaker operators and sensors. A DAG can start training or processing jobs, wait for completion, and deploy the resulting model.
Amazon NeptuneNeptune ML uses SageMaker to train and host graph machine learning models. Neptune queries can use these models for tasks such as predicting missing relationships or classifying graph nodes.
Amazon OpenSearch ServiceOpenSearch Service supports machine learning connectors that invoke models hosted on SageMaker endpoints. Search and ingest pipelines can use these models to generate embeddings or enrich documents.
Amazon Q
Amazon Q Developer is integrated into SageMaker development environments to assist with code, explanations, and troubleshooting. In Unified Studio, it also assists with analytics and data development tasks.
Amazon QuickSightQuickSight can augment datasets with predictions from supported SageMaker models. For example, a dataset can gain a predicted churn score before it is used in dashboards.
Amazon RDSSageMaker Data Wrangler can import data from supported RDS databases through database connections. Supported RDS for MySQL databases can also provide operational data to SageMaker Lakehouse through zero-ETL integrations.
Amazon RekognitionSageMaker Canvas exposes ready-to-use image analysis models powered by Rekognition. Users can detect objects or extract text from images without building an image model themselves.
Amazon SageMaker Data Wrangler
Data Wrangler prepares and transforms data for SageMaker machine learning workflows. Its processing flows can be exported into SageMaker jobs and pipelines to repeat the same preparation during training.
Amazon SageMaker Ground TruthGround Truth creates labeled datasets for training SageMaker models using human workers and automated labeling. Its output manifests can be consumed by SageMaker training workflows.
Amazon Security Lake
SageMaker can consume Security Lake data through a subscriber integration to build and train security-focused machine learning models. This supports tasks such as analyzing normalized security events for anomalous activity.
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Amazon Simple Notification Service (Amazon SNS)SageMaker asynchronous inference can publish success and failure notifications to SNS topics. Ground Truth also uses SNS topics for streaming labeling inputs and labeling results.
Amazon TextractSageMaker Canvas provides ready-to-use document analysis capabilities powered by Textract. Users can extract information from documents, receipts, and identity documents without training their own extraction model.
Amazon VPCSageMaker resources can connect to customer VPC subnets and use security groups to control network access. Studio can also operate in VPC-only mode so application traffic uses customer-controlled networking.
AWS Auto ScalingSageMaker inference endpoints integrate with Application Auto Scaling to adjust provisioned inference capacity. Scaling policies can respond to metrics such as invocations per instance or follow a schedule.
AWS Clean Rooms
AWS Clean Rooms ML integrates with SageMaker AI for custom machine learning workflows on collaboration data. Collaborators can train custom models while applying Clean Rooms controls to the underlying data.
AWS CloudFormationCloudFormation can provision SageMaker resources, including domains, notebook instances, models, endpoints, and pipelines. This allows machine learning infrastructure to be deployed and updated as versioned templates.
AWS CloudTrailSageMaker API activity is recorded in CloudTrail for auditing and investigation. Supported SageMaker data events can also capture activity such as model endpoint invocations when data-event logging is configured.
AWS CodeArtifactSageMaker Studio environments can be configured to install Python packages from CodeArtifact repositories. This provides controlled access to private dependencies used in notebooks, training code, and other development workflows.
AWS CodeBuildSageMaker Projects MLOps templates use CodeBuild to build and test machine learning artifacts, including custom container images. These builds can feed model-building and deployment pipelines.
AWS CodeCommitSageMaker notebook instances and Studio support Git-based workflows with CodeCommit repositories. SageMaker Projects templates can also use CodeCommit to store model-building and deployment code.
AWS CodeConnections
SageMaker Projects templates can use CodeConnections connections to access supported external Git repositories through CodePipeline. Repository changes can then initiate the project's model-building or deployment workflow.
AWS CodePipelineSageMaker Projects uses CodePipeline in MLOps templates to automate model building and deployment. Pipelines can coordinate source changes, builds, approval steps, and deployment to SageMaker endpoints.
AWS ConfigAWS Config records supported SageMaker resource configurations and evaluates them against compliance rules. Managed rules can check notebook settings such as encryption, direct internet access, and root access.
AWS Deep Learning ContainersSageMaker supports AWS Deep Learning Containers as managed training and inference environments. These images supply framework runtimes and dependencies for workloads such as PyTorch and TensorFlow training.
AWS DeepRacerDeepRacer uses SageMaker infrastructure to train reinforcement learning models. Training produces driving policies that can be evaluated in simulation and deployed to a DeepRacer vehicle.
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AWS IAM Identity Center (AWS Single Sign-On)SageMaker domains can use IAM Identity Center to authenticate users and provide access to Studio. Trusted identity propagation can carry the user's identity into supported analytics services for fine-grained authorization.
AWS IoT GreengrassModels developed with SageMaker can be packaged and deployed to Greengrass devices for local inference. SageMaker Neo-compiled models can use the Neo runtime on supported edge devices.
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AWS Key Management Service (AWS KMS)SageMaker uses KMS keys to encrypt supported storage volumes, model artifacts, job outputs, and other protected resources. Customer-managed keys let organizations control access to encrypted machine learning data.
AWS Lake FormationSageMaker integrates with Lake Formation for governed access to lake data. Supported Studio connections can propagate user identities so Lake Formation permissions apply to notebook queries.
AWS MarketplaceSageMaker can use machine learning algorithms and model packages subscribed to through AWS Marketplace. Compatible products can be used for managed training or deployed to SageMaker inference endpoints.
AWS PrivateLinkSageMaker supports interface VPC endpoints through PrivateLink for supported APIs, inference, and Studio connectivity. This lets clients access SageMaker privately without sending traffic through the public internet.
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AWS Resource Access Manager (AWS RAM)SageMaker Feature Store integrates with AWS RAM to share supported feature resources across accounts. This allows teams to discover and reuse centrally managed machine learning features.
AWS Secrets ManagerSageMaker uses Secrets Manager for supported connection credentials, including database connections used by Data Wrangler and repository credentials used by development workflows. Secrets can be referenced without embedding passwords or tokens in notebooks.
AWS Security HubSecurity Hub includes security controls for SageMaker notebook configurations. Findings can identify settings such as enabled direct internet access, enabled root access, or missing KMS encryption.
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AWS Security Token Service (AWS STS)SageMaker assumes execution roles through STS to obtain temporary credentials for jobs and hosted workloads. Those credentials authorize access to resources such as training data in S3 and container images in ECR.
AWS Service CatalogSageMaker Projects uses Service Catalog products to provision standardized MLOps environments. Administrators can publish approved project templates that create repositories, pipelines, and associated infrastructure.
AWS Systems ManagerSageMaker supports remote debugging of training containers through Systems Manager Session Manager. Authorized developers can open a secure session to inspect a running training environment without configuring inbound SSH access.
AWS Trusted AdvisorTrusted Advisor includes SageMaker checks that identify potentially idle or underutilized resources, such as notebook instances and inference endpoints. These recommendations help locate machine learning resources that may be generating unnecessary costs.
