Category: Amazon SageMaker

With Amazon SageMaker Lakehouse unified data connectivity, you can confidently connect, explore, and unlock the full value of your data across AWS services and achieve your business objectives with agility. This post demonstrates how SageMaker Lakehouse unified data connectivity helps your data integration workload by streamlining the establishment and management of connections for various data sources.

Amazon SageMaker Unified Studio, in preview, is an integrated development environment (IDE) for data, analytics, and AI. Discover your data and put it to work using familiar AWS tools to complete end-to-end development workflows, including data analysis, data processing, model training, generative AI app building, and more, in a single governed environment. This post demonstrates how SageMaker Unified Studio unifies your analytic workloads.

Amazon SageMaker Lakehouse offers a unified solution for enterprise data access, combining data from warehouses and lakes. This post demonstrates how SageMaker Lakehouse integrates scattered data sources, enabling secure enterprise-wide access, and allowing teams to use their preferred tools for predicting and analyzing customer churn. The solution involves multiple data sources, including Amazon S3, Amazon Redshift, and AWS Glue Data Catalog, with AWS Lake Formation managing permissions.

Amazon SageMaker Unified Studio (preview) provides an integrated data and AI development environment within Amazon SageMaker. This post shows how you can build a low-code and no-code (LCNC) visual ETL flow that enables seamless data ingestion and transformation across multiple data sources.

AWS has introduced zero-ETL integration support from external applications to AWS Glue, simplifying data integration for organizations. This new feature allows for seamless replication of data from popular platforms like Salesforce, ServiceNow, and Zendesk into Amazon SageMaker Lakehouse and Amazon Redshift. This blog post demonstrates a use case involving ServiceNow data integration, outlining the process of setting up a connector, creating a zero-ETL integration, and verifying both initial data load and change data capture (CDC). It also highlights the advantages of using Apache Iceberg for data versioning and time travel capabilities within zero-ETL integrations.

In this post, we show how to connect to, govern, and run federated queries on data stored in Redshift, DynamoDB (Preview), and Snowflake (Preview). To query our data, we use Athena, which is seamlessly integrated with SageMaker Unified Studio. We use SageMaker Lakehouse to present data to end-users as federated catalogs, a new type of catalog object. Finally, we demonstrate how to use column-level security permissions in AWS Lake Formation to give analysts access to the data they need while restricting access to sensitive information.

This week on the keynote stages at AWS re:Invent 2024, you heard from Matt Garman, CEO, AWS, and Swami Sivasubramanian, VP of AI and Data, AWS, speak about the next generation of Amazon SageMaker, the center for all of your data, analytics, and AI. This update addresses the evolving relationship between analytics and AI workloads, aiming to streamline how customers work with their data. It helps organizations collaborate more effectively, reduce data silos, and accelerate the development of AI-powered applications while maintaining robust governance and security measures.

In this post, instead of using the BM25 algorithm, we introduce sparse vector retrieval. This approach offers improved term expansion while maintaining interpretability. We walk through the steps of integrating sparse and dense vectors for knowledge retrieval using Amazon OpenSearch Service and run some experiments on some public datasets to show its advantages.

A protein is a sequence of amino acids that, when chained together, creates a 3D structure. This 3D structure allows the protein to bind to other structures within the body and initiate changes. This binding is core to the working of many drugs. A common workflow within drug discovery is searching for similar proteins, because […]

In this post, we show how to build a Q&A bot with RAG (Retrieval Augmented Generation). RAG uses data sources like Amazon Redshift and Amazon OpenSearch Service to retrieve documents that augment the LLM prompt. For getting data from Amazon Redshift, we use the Anthropic Claude 2.0 on Amazon Bedrock, summarizing the final response based on pre-defined prompt template libraries from LangChain. To get data from Amazon OpenSearch Service, we chunk, and convert the source data chunks to vectors using Amazon Titan Text Embeddings model.