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Scale ML workflows without moving data
Managing data across platforms introduces complexity, slowing down model development and increasing operational overhead. This tutorial from AWS shows how Amazon Redshift, Amazon Bedrock, and Databricks work together to:
- Query data efficiently: Access Amazon Redshift directly from Databricks notebooks without data movement or complex ETL pipelines
- Automate vectorization: Transform structured data into vector embeddings for semantic search and AI applications
- Scale RAG applications: Integrate Amazon Bedrock foundation models with your data infrastructure for production‑ready retrieval‑augmented generation
If your challenge is optimizing data access and ML workflows, this tutorial provides practical solutions to improve efficiency.
Read the tutorial › |
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Databricks
Try Databricks Data Intelligence Platform in AWS Marketplace.
Start your free trial ›
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About AWS Marketplace:
Visit AWS Marketplace for help finding the best solutions for your use case and add new capabilities to your tech stack with your AWS account:
- Tools are designed to plug in to your AWS workflows and integrate with your favorite AWS services
- Evaluate fast with free trials and developer‑tier pricing to support fast prototyping
- Avoid upfront license fees and pay only for what you use, consolidating billing with your AWS account
- A broad selection of tools across AI, data, observability, security and more can enhance how you build with AWS
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