AWS Machine Learning Blog
Author: Shreyas Subramanian
Shreyas Subramanian is a Principal data scientist and helps customers by using Machine Learning to solve their business challenges using the AWS platform. Shreyas has a background in large scale optimization and Machine Learning, and in use of Machine Learning and Reinforcement Learning for accelerating optimization tasks.
Use custom metrics to evaluate your generative AI application with HAQM Bedrock
Now with HAQM Bedrock, you can develop custom evaluation metrics for both model and RAG evaluations. This capability extends the LLM-as-a-judge framework that drives HAQM Bedrock Evaluations. In this post, we demonstrate how to use custom metrics in HAQM Bedrock Evaluations to measure and improve the performance of your generative AI applications according to your specific business requirements and evaluation criteria.
Use HAQM Bedrock Intelligent Prompt Routing for cost and latency benefits
Today, we’re happy to announce the general availability of HAQM Bedrock Intelligent Prompt Routing. In this blog post, we detail various highlights from our internal testing, how you can get started, and point out some caveats and best practices. We encourage you to incorporate HAQM Bedrock Intelligent Prompt Routing into your new and existing generative AI applications.
Optimize reasoning models like DeepSeek with Prompt Optimization on HAQM Bedrock
In this post, we demonstrate how to optimize reasoning models like DeepSeek-R1 using prompt optimization on HAQM Bedrock.
Improve the performance of your Generative AI applications with Prompt Optimization on HAQM Bedrock
Today, we are excited to announce the availability of Prompt Optimization on HAQM Bedrock. With this capability, you can now optimize your prompts for several use cases with a single API call or a click of a button on the HAQM Bedrock console. In this post, we discuss how you can get started with this new feature using an example use case in addition to discussing some performance benchmarks.
Build cost-effective RAG applications with Binary Embeddings in HAQM Titan Text Embeddings V2, HAQM OpenSearch Serverless, and HAQM Bedrock Knowledge Bases
Today, we are happy to announce the availability of Binary Embeddings for HAQM Titan Text Embeddings V2 in HAQM Bedrock Knowledge Bases and HAQM OpenSearch Serverless. This post summarizes the benefits of this new binary vector support and gives you information on how you can get started.
Build powerful RAG pipelines with LlamaIndex and HAQM Bedrock
In this post, we show you how to use LlamaIndex with HAQM Bedrock to build robust and sophisticated RAG pipelines that unlock the full potential of LLMs for knowledge-intensive tasks.
Get started with HAQM Titan Text Embeddings V2: A new state-of-the-art embeddings model on HAQM Bedrock
Embeddings are integral to various natural language processing (NLP) applications, and their quality is crucial for optimal performance. They are commonly used in knowledge bases to represent textual data as dense vectors, enabling efficient similarity search and retrieval. In Retrieval Augmented Generation (RAG), embeddings are used to retrieve relevant passages from a corpus to provide […]
Modular functions design for Advanced Driver Assistance Systems (ADAS) on AWS
Over the last 10 years, a number of players have developed autonomous vehicle (AV) systems using deep neural networks (DNNs). These systems have evolved from simple rule-based systems to Advanced Driver Assistance Systems (ADAS) and fully autonomous vehicles. These systems require petabytes of data and thousands of compute units (vCPUs and GPUs) to train. This […]
Intelligently search your Jira projects with HAQM Kendra Jira cloud connector
July 2023: This post was reviewed for accuracy. Organizations use agile project management platforms such as Atlassian Jira to enable teams to collaborate to plan, track, and ship deliverables. Jira captures organizational knowledge about the workings of the deliverables in the issues and comments logged during project implementation. However, making this knowledge easily and securely […]
Automatically detect sports highlights in video with HAQM SageMaker
July 2023: Please refer to the Media Replay Engine (MRE) solution presented in this Github repo instead, for the latest and more efficient solution for this use case. MRE is a framework for building automated video clipping and replay (highlight) generation pipelines using AWS services for live and video-on-demand (VOD) content. Extracting highlights from a […]