Tag: ai/ml

Connecting AI agents to your enterprise data shouldn’t require complex custom integrations or weeks of development. With the release of fully managed, remote Model Context Protocol (MCP) servers for Google services last month, you can now use BigQuery MCP server to give your AI agents a direct, secure, way to analyze data. This fully managed …

Read more “Build data analytics agents faster with BigQuery’s fully managed, remote MCP server”

NVIDIA founder and CEO Jensen Huang took the stage at the Fontainebleau Las Vegas today to open CES 2026, declaring that AI is scaling into every domain and every device. “Computing has been fundamentally reshaped as a result of accelerated computing, as a result of artificial intelligence,” Huang said. “What that means is some $10 …

Read more “NVIDIA Rubin Platform, Open Models, Autonomous Driving: NVIDIA Presents Blueprint for the Future at CES”

Operating a self-managed MLflow tracking server comes with administrative overhead, including server maintenance and resource scaling. As teams scale their ML experimentation, efficiently managing resources during peak usage and idle periods is a challenge. Organizations running MLflow on Amazon EC2 or on-premises can optimize costs and engineering resources by using Amazon SageMaker AI with serverless …

Read more “Migrate MLflow tracking servers to Amazon SageMaker AI with serverless MLflow”

Intelligent Document Processing (IDP) transforms how organizations handle unstructured document data, enabling automatic extraction of valuable information from invoices, contracts, and reports. Today, we explore how to programmatically create an IDP solution that uses Strands SDK, Amazon Bedrock AgentCore, Amazon Bedrock Knowledge Base, and Bedrock Data Automation (BDA). This solution is provided through a Jupyter notebook that enables users …

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At Amazon, our culture, built on honest and transparent discussion of our growth opportunities, enables us to focus on investing and innovating to continually raise the standard on our ability to deliver value for our customers. Earlier this month, we had the opportunity to share an example of this process at work in Mantle, our …

Read more “Exploring the zero operator access design of Mantle”

As organizations scale their generative AI implementations, the critical challenge of balancing quality, cost, and latency becomes increasingly complex. With inference costs dominating 70–90% of large language model (LLM) operational expenses, and verbose prompting strategies inflating token volume by 3–5x, organizations are actively seeking more efficient approaches to model interaction. Traditional prompting methods, while effective, …

Read more “Move Beyond Chain-of-Thought with Chain-of-Draft on Amazon Bedrock”