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Model Context Protocol (MCP) integration in Amazon Quick transforms how financial analysts access time-series market intelligence, removing the need for complex database queries. As a financial analyst, you navigate millions of stock trades flowing through markets every second, searching for patterns that drive trading decisions. Financial institutions often use time series databases to analyze high-frequency …

Amazon Quick integration with time-series databases for market intelligence using MCP Read More »

Learn how Googlers used AI to produce Google I/O 2026. ​ 

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, …

Comprehensive observability for Amazon SageMaker AI LLM inference: From GPU utilization to LLM quality Read More »

We used Google AI Studio to vibe code a quiz about our top I/O 2026 announcements. ​ 

Watch 11 videos showing the capabilities of Gemini Omni and Gemini 3.5, announced at Google I/O 2026. ​ 

University of Waterloo students develop AI prototypes like sign language tutors to reshape the future of education and work. ​ 

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 …

Training Azerbaijani language models on Amazon SageMaker AI Read More »

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 …

Build a custom portal with embedded Amazon SageMaker AI MLflow Apps Read More »

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 »

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 …

Evaluating Deep Agents using LangSmith on AWS Read More »