How we used Gemini to build Google I/O 2026
Learn how Googlers used AI to produce Google I/O 2026.
Comprehensive observability for Amazon SageMaker AI LLM inference: From GPU utilization to LLM quality
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 over time as input distributions shift, …
Take our I/O 2026 quiz, vibe coded in Google AI Studio.
We used Google AI Studio to vibe code a quiz about our top I/O 2026 announcements.
11 demos of Gemini Omni and Gemini 3.5 in action
Watch 11 videos showing the capabilities of Gemini Omni and Gemini 3.5, announced at Google I/O 2026.
Check out real-life AI prototypes from the Futures Lab.
University of Waterloo students develop AI prototypes like sign language tutors to reshape the future of education and work.
Training Azerbaijani language models on Amazon SageMaker AI
This solution builds on open source tools including PyTorch, Hugging Face Transformers, and Liger Kernels. The authors would also like to thank Aiham Taleb, Arefeh Ghahvechi, Manav Choudhary, Rohit Thekkanal, Daz Akbarov, Jamila Jamilova, Ross Povelikin, Almas Moldakanov, Christelle Xu, and Ivan Khvostishkov for their contributions in making this project possible. Azercell Telecom LLC, Azerbaijan’s …
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Build a custom portal with embedded Amazon SageMaker AI MLflow Apps
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 who rely on SSO-integrated internal portals …
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Streamline external access to Amazon SageMaker MLflow using a REST API proxy
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 their established systems while maintaining their …
Streamline external access to Amazon SageMaker MLflow using a REST API proxy Read More »
Evaluating Deep Agents using LangSmith on AWS
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 entire workflow. LangSmith on AWS gives …
Build a test suite that grows with your agent with dataset management in Amazon Bedrock AgentCore
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 the discipline of versioned test fixtures …