Leveraging vast unstructured data poses challenges, particularly for global businesses needing cross-language data search. In Part 1 of this blog series, we built the architectural foundation for the content repository. The key component of Part 1 was the dynamic access control-based logic with a web UI to upload documents.

June 30, 2025 - For this, we use the explainability features of SageMaker Clarify. Let’s dive deeper and explore the solution architecture for each of the four workflows for data prep, train and tune, deploy, and finally a pipeline that ties everything together in an automated fashion up to storing the models in a registry.

Experienced ML practitioners can deploy their own models with customized settings for their application needs using the ModelBuilder class in the SageMaker AI Python SDK, which provides fine-grained control over various settings, such as instance types, network isolation, and resource allocation. Use case 3: Deploy machine learning models at scale. For advanced users and organizations who want to manage models at scale in production, use the AWS SDK for Python (Boto3) and CloudFormation along with your desired Infrastructure as Code (IaC) and CI/CD tools to provision resources and automate resource management.

July 19, 2020 - Finally, the SageMaker API is directly available from the AWS CLI. In this post we’ve discussed the SageMaker architecture. We’ve looked at the AWS services that compose SageMaker and how these services are tied together. We also examined various ways to call the SageMaker API including the Python SDK, the boto3 library, and the command line interface.

February 13, 2023 - AWS Sagemaker — Reference Architecture for MLOps Machine learning (ML) life cycle starts with Data, We create a separate account for all datastore. The data store can be S3, EFS, DynamoDB, RDS …

Amazon SageMaker Instances and Accelerators for · ML Inference · • · Conclusion · © 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Confidential and Trademark. The AWS ML Stack · Broadest and most complete set of machine learning capabilities ·

1 week ago - Unify data access across Amazon S3 data lakes, Amazon Redshift data warehouses, and third-party and federated data sources with a lakehouse architecture in Amazon SageMaker ... Bringing together widely adopted AWS machine learning (ML) and analytics capabilities, the next generation of Amazon ...

March 14, 2025 - As part of the onboarding process, the admin onboards single sign-on (SSO) users, SSO groups, and IAM users who are authorized to log in to SageMaker Unified Studio. IAM roles can be onboarded on the domain as well, but can be used for programmatic access only. During the quick setup deployment of the domain, default project profile templates are created. A project profile is a collection of blueprints that holds configurations of AWS tools and services.

The lakehouse architecture of Amazon SageMaker is a unified data architecture built on AWS's cloud-native infrastructure that bridges Amazon S3 data lakes and Amazon Redshift data warehouses into a cohesive analytics platform.

August 12, 2025 - AWS List API calls can’t automatically filter results using tags, which might impact resource discovery and management at scale. Our AIOps architecture illustrates SageMaker Unified Studio Project A spanning across three lines of business (DEV, TEST, and PROD), representing the different software development lifecycle (SDLC) stages.

March 27, 2024 - On the Components and registries menu, you can access a set of purpose-built functionalities that simplify your ML development experience with Amazon SageMaker; for example, you can review model versions registered in SageMaker Model Registry, or track the runs of ML pipelines run with Amazon SageMaker Pipelines. Now, let’s understand how Studio Classic notebooks are designed, with the help of a highly simplified version of the following architecture diagram (click for an enlarged view).

July 10, 2023 - This will give you the ability to take advantage of cloud-native ML with Amazon SageMaker. To help address these challenges, we worked on outlining an end-to-end system architecture in Figure 1 that defines: 1) connectivity between on-premises data centers and AWS Regions; 2) mappings for on-premises data to the cloud; and 3) Aligning Amazon SageMaker to appropriate storage, based on ML requirements.

For Region availability, see AWS services by Region · . For specific endpoints, see the Service endpoints and quotas page, and choose the link for the service. ... The target architecture integrates Azure DevOps with Amazon SageMaker AI, creating a cross-cloud ML workflow.

AWS Reference Architecture · AWS IAM Identity Center manages user access · and SSO to Amazon SageMaker Unified Studio for · data engineers. SageMaker Unified Studio allows data engineers · and data analysts to collaborate on the sales · forecasting project.

1 week ago - Discover your data and put it to work using familiar AWS tools for complete development workflows, including model development, generative AI app development, data processing, and SQL analytics, in a single governed environment. Create or join projects to collaborate with your teams, securely share AI and analytics artifacts, and access your data stored in Amazon Simple Storage Service (Amazon S3), Amazon Redshift, and more data sources through the Amazon SageMaker open lakehouse architecture...

NeuralMesh: Accelerated Infrastructure Solutions for Agentic AI | WEKA

January 29, 2024 - For edge deployment, we automate the process with Step Functions and utilize AWS IoT Greengrass as our edge device runtime environment. Alternatively, we can deploy the model as SageMaker endpoints in the cloud for flexibility. This architecture minimizes operational effort by leveraging managed and serverless services while maintaining the integrity of our MLOps pipeline.

1 week ago - The next generation of Amazon SageMaker is built on an open lakehouse architecture, fully compatible with Apache Iceberg.

October 17, 2025 - This architecture diagram shows how Amazon SageMaker Unified Studio enables a collaborative data engineering and analytics experience for sales forecasting using a Lakehouse architecture, web-based studio with generative AI, and orchestration tools in a unified portal.

1 week ago - SageMaker provides built-in monitoring tools that track the model's performance metrics and detect anomalies. Model Management: Finally, once the model is in production, it's important to manage it over time. This includes tasks such as updating the model with new data, retraining the model periodically, and ensuring that it remains performant over time. One of the most critical architectural decisions in SageMaker is how to deploy your model.