Favorite Deploying large language models (LLMs) at scale on Amazon SageMaker AI Inference makes observability a critical pillar of any production machine learning (ML) strategy. Unlike conventional software that returns deterministic outputs, LLMs generate variable, free-form responses that are difficult to validate with standard metrics. LLM output quality can change
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Shared by AWS Machine Learning May 30, 2026
Favorite We used Google AI Studio to vibe code a quiz about our top I/O 2026 announcements. View Original Source (blog.google/technology/ai/) Here.
Favorite Watch 9 videos showing the capabilities of Gemini Omni and Gemini 3.5, announced at Google I/O 2026. View Original Source (blog.google/technology/ai/) Here.
Favorite University of Waterloo students develop AI prototypes like sign language tutors to reshape the future of education and work. View Original Source (blog.google/technology/ai/) Here.
Favorite Financial institutions running on AWS and Snowflake benefit from a deeply integrated framework that combines Snowflake’s AI Data Cloud with AWS cloud infrastructure, including integrations with AWS services such as Amazon Simple Storage Service (Amazon S3), AWS Glue, Amazon SageMaker, and Amazon Bedrock. With over 50 native integrations between
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Shared by AWS Machine Learning May 29, 2026
Favorite Today, we’re excited to announce the availability of Anthropic’s most advanced Opus model, Claude Opus 4.8, on Amazon Bedrock and the Claude Platform on AWS. Claude Opus 4.8 represents a meaningful step forward, delivering improvements across the workflows teams run in production, from agentic coding and deep knowledge work
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Shared by AWS Machine Learning May 29, 2026
Favorite Agent evaluation is most powerful when you combine fast-moving online signals with stable offline baselines. To understand whether your agent is truly improving over time, you need a fixed benchmark alongside your changing real-world traffic. Managing test cases for evaluation baselines as a dataset in Amazon Bedrock AgentCore brings
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Shared by AWS Machine Learning May 29, 2026
Favorite This post was co-authored with Karan Singh, Head of Partnerships at LangChain Validating AI agent behavior before production is one of the hardest problems in applied AI. Agents are non-deterministic, multi-step where errors in early steps can affect downstream results. A single bad tool call can cascade through an
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Shared by AWS Machine Learning May 29, 2026
Favorite Machine learning (ML) teams use MLflow to manage their ML lifecycle effectively. Amazon SageMaker MLflow provides comprehensive ML experiment tracking and model management capabilities. However, many enterprises have existing infrastructure requirements that need HTTPS-based integrations rather than direct SDK usage. Many organizations need to integrate Amazon SageMaker MLflow with
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Shared by AWS Machine Learning May 29, 2026
Favorite As ML teams grow, embedding Amazon SageMaker AI MLflow Apps into a custom portal requires a scalable approach to access management. Distributing presigned URLs doesn’t scale for teams with dozens of data scientists, and granting individual AWS Management Console access adds operational overhead for administrators managing access controls. Teams
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Shared by AWS Machine Learning May 29, 2026
Shared by AWS Machine Learning May 30, 2026
