Bringing the power of Personal Intelligence to more people
Favorite We’re expanding Personal Intelligence across AI Mode in Search, the Gemini app and Gemini in Chrome. View Original Source (blog.google/technology/ai/) Here.
Our latest investment in open source security for the AI era
Favorite Google is making new investments, building new tools and developing code security to improve open source security. View Original Source (blog.google/technology/ai/) Here.
Build an offline feature store using Amazon SageMaker Unified Studio and SageMaker Catalog
Favorite Building and managing machine learning (ML) features at scale is one of the most critical and complex challenges in modern data science workflows. Organizations often struggle with fragmented feature pipelines, inconsistent data definitions, and redundant engineering efforts across teams. Without a centralized system for storing and reusing features, models
Read More
Shared by AWS Machine Learning March 16, 2026
How Workhuman built multi-tenant self-service reporting using Amazon Quick Sight embedded dashboards
Favorite This post is cowritten with Ilija Subanovic and Michael Rice from Workhuman. Workhuman’s customer service and analytics team were drowning in one-time reporting requests from seven million users worldwide—a common challenge with legacy reporting tools at scale. Business intelligence (BI) admins faced mounting pressure as their teams became overwhelmed
Read More Shared by AWS Machine Learning March 16, 2026
Introducing Disaggregated Inference on AWS powered by llm-d
Favorite We thank Greg Pereira and Robert Shaw from the llm-d team for their support in bringing llm-d to AWS. In the agentic and reasoning era, large language models (LLMs) generate 10x more tokens and compute through complex reasoning chains compared to single-shot replies. Agentic AI workflows also create highly variable
Read More Shared by AWS Machine Learning March 16, 2026
Agentic AI in the Enterprise Part 2: Guidance by Persona
Favorite This is Part II of a two-part series from the AWS Generative AI Innovation Center. If you missed Part I, refer to Operationalizing Agentic AI Part 1: A Stakeholder’s Guide. The biggest barrier to agentic AI isn’t the technology—it’s the operating model. In Part I, we established that organizations
Read More Shared by AWS Machine Learning March 16, 2026
AWS and NVIDIA deepen strategic collaboration to accelerate AI from pilot to production
Favorite AI is moving fast, and for most of our customers, the real opportunity isn’t in experimenting with it—it’s in running AI in production where it drives meaningful business outcomes. This means building systems that run reliably, perform at scale, and meet your organization’s security and compliance requirements. Today at
Read More Shared by AWS Machine Learning March 16, 2026
Fine-tuning NVIDIA Nemotron Speech ASR on Amazon EC2 for domain adaptation
Favorite This post is a collaboration between AWS, NVIDIA and Heidi. Automatic speech recognition (ASR), often called speech-to-text (STT) is becoming increasingly critical across industries like healthcare, customer service, and media production. While pre-trained models offer strong capabilities for general speech, fine-tuning for specific domains and use cases can enhance
Read More Shared by AWS Machine Learning March 14, 2026
Multimodal embeddings at scale: AI data lake for media and entertainment workloads
Favorite This post shows you how to build a scalable multimodal video search system that enables natural language search across large video datasets using Amazon Nova models and Amazon OpenSearch Service. You will learn how to move beyond manual tagging and keyword-based searches to enable semantic search that captures the
Read More Shared by AWS Machine Learning March 13, 2026
Secure AI agents with Policy in Amazon Bedrock AgentCore
Favorite Deploying AI agents safely in regulated industries is challenging. Without proper boundaries, agents that access sensitive data or execute transactions can pose significant security risks. Unlike traditional software, an AI agent chooses actions to achieve a goal by invoking tools, accessing data, and adapting its reasoning using data from
Read More Shared by AWS Machine Learning March 13, 2026