Artificial Intelligence
Author: Mark Roy
Mark Roy is a Principal Machine Learning Architect for AWS, helping customers design and build generative AI solutions. His focus since early 2023 has been leading solution architecture efforts for the launch of Amazon Bedrock, the flagship generative AI offering from AWS for builders. Mark’s work covers a wide range of use cases, with a primary interest in generative AI, agents, and scaling ML across the enterprise. He has helped companies in insurance, financial services, media and entertainment, healthcare, utilities, and manufacturing. Prior to joining AWS, Mark was an architect, developer, and technology leader for over 25 years, including 19 years in financial services. Mark holds six AWS Certifications, including the ML Specialty Certification.
Harness the power of MCP servers with Amazon Bedrock Agents
Today, MCP is providing agents standard access to an expanding list of accessible tools that you can use to accomplish a variety of tasks. In this post, we show you how to build an Amazon Bedrock agent that uses MCP to access data sources to quickly build generative AI applications.
Use RAG for drug discovery with Amazon Bedrock Knowledge Bases
Amazon Bedrock provides a broad range of models from Amazon and third-party providers, including Anthropic, AI21, Meta, Cohere, and Stability AI, and covers a wide range of use cases, including text and image generation, embedding, chat, high-level agents with reasoning and orchestration, and more. Amazon Bedrock Knowledge Bases allows you to build performant and customized […]
Use streaming ingestion with Amazon SageMaker Feature Store and Amazon MSK to make ML-backed decisions in near-real time
August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, or even dynamically pricing products […]
Scale ML feature ingestion using Amazon SageMaker Feature Store
Amazon SageMaker Feature Store is a purpose-built solution for machine learning (ML) feature management. It helps data science teams reuse ML features across teams and models, serves features for model predictions at scale with low latency, and train and deploy new models more quickly and effectively. As you learn about how to use a feature […]
How Thomson Reuters accelerated research and development of natural language processing solutions with Amazon SageMaker
This post is co-written by John Duprey and Filippo Pompili from Thomson Reuters. Thomson Reuters (TR) is one of the world’s most trusted providers of answers, helping professionals make confident decisions and run better businesses. Teams of experts from TR bring together information, innovation, and confident insights to unravel complex situations, and their worldwide network […]
Save on inference costs by using Amazon SageMaker multi-model endpoints
Businesses are increasingly developing per-user machine learning (ML) models instead of cohort or segment-based models. They train anywhere from hundreds to hundreds of thousands of custom models based on individual user data. For example, a music streaming service trains custom models based on each listener’s music history to personalize music recommendations. A taxi service trains […]
Identifying bird species on the edge using the Amazon SageMaker built-in Object Detection algorithm and AWS DeepLens
April 2023 Update: Starting January 31, 2024, you will no longer be able to access AWS DeepLens through the AWS management console, manage DeepLens devices, or access any projects you have created. To learn more, refer to these frequently asked questions about AWS DeepLens end of life. Custom object detection has become an important enabler for […]