A Technology Broker and Management Company Specializing in KM Systems
Favorite Amazon SageMaker JumpStart is a machine learning (ML) hub within Amazon SageMaker AI that provides access to a curated catalog of AI models, solution templates, and example notebooks. It reduces the time it takes to go from model selection to deployment. You can use it to evaluate open source
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Shared by AWS Machine Learning October 20, 2026
Favorite Today, we’re excited to announce that new Amazon SageMaker AI customers can now land inside a fully configured Amazon SageMaker Studio environment in only a few steps after signing up for AWS. You can use a sign-in method that you already have, such as Amazon, Apple, GitHub, Google, or
Read More Shared by AWS Machine Learning October 20, 2026
Favorite Amazon Bedrock now supports structured outputs for all Anthropic Claude models, on both the bedrock-runtime and bedrock-mantle endpoints, where the model is available. Whether you call Claude through Boto3 and the bedrock-runtime APIs or through the Messages API and the bedrock-mantle endpoint, you can now receive schema-compliant JSON responses
Read More Shared by AWS Machine Learning October 20, 2026
Favorite In the first post in this series, we built a semantic layer on AWS using Stardog’s knowledge graph over Amazon Aurora and Amazon Redshift. We ran a Strands agent on Amazon Bedrock AgentCore against that layer. The second post kept the same customer 360 (C360) pattern and swapped in
Read More Shared by AWS Machine Learning October 20, 2026
Favorite This post is co-written with Zhili Cheng, Principal Product Manager, George Earle, Director, AI Engineering, and Kyler K, Sales Engineer of Strategy. In the first post in this series, we built a semantic layer on AWS using Stardog’s knowledge graph over Amazon Aurora and Amazon Redshift and ran a
Read More Shared by AWS Machine Learning October 20, 2026
Favorite Agentic AI applications need governed access to live enterprise data. That data can live in operational systems, analytical warehouses, SaaS applications, other clouds, or on premises. In the previous post in this series, we built a semantic layer on AWS using Stardog’s knowledge graph over Amazon Aurora and Amazon
Read More Shared by AWS Machine Learning October 20, 2026
Favorite This is a guest post co-written with Roei Zavida from Eon. Independent software vendors (ISVs) building AI-powered products need to connect agents and humans to dozens of operational tools across clouds, each with different authentication models, network boundaries, and access policies. When those tools span AWS, GCP, and Azure
Read More Shared by AWS Machine Learning October 20, 2026
Favorite When AI agents access databases, APIs, and sensitive business operations, establishing cryptographic identity through X.509 public key infrastructure (PKI) becomes a critical requirement. The core challenge is proving which agent made a request, preventing forgery between agents, and confirming that each agent acts only on behalf of an authorized
Read More Shared by AWS Machine Learning October 20, 2026
Favorite Runtime discovery with AWS Agent Registry can help address a problem that agent demos might hide. Hardcoded tools work fine for a handful, start to degrade at a few dozen, and become unmanageable beyond that. Enterprise systems expose hundreds of internal APIs for pricing, support, orders, inventory, and customer
Read More Shared by AWS Machine Learning October 20, 2026
Favorite Canadian organizations building generative AI applications on Amazon Bedrock often need the latest foundation models (FMs), such as OpenAI GPT-6.1, OpenAI GPT-6, and GPT-5.6, accessible from the Canadian AWS Regions. With Amazon Bedrock cross-Region inference (CRIS), you can now access these models from Canada. OpenAI GPT-5.6 models (Sol, Terra,
Read More Shared by AWS Machine Learning October 20, 2026