Improve accuracy of Amazon Rekognition Face Search with user vectors In various industries, such as financial services, telecommunications, and healthcare, customers use a digital identity process, which usually involves several steps to verify end-users during online onboarding or step-up authentication. An example of one step that can be used is face search, which can help determine whether a new end-user’s face matches those associated with […]
https://aws.amazon.com/blogs/m....achine-learning/impr

Improve LLM performance with human and AI feedback on Amazon SageMaker for Amazon Engineering The Amazon EU Design and Construction (Amazon D&C) team is the engineering team designing and constructing Amazon warehouses. The team navigates a large volume of documents and locates the right information to make sure the warehouse design meets the highest standards. In the post A generative AI-powered solution on Amazon SageMaker to help Amazon EU […]
https://aws.amazon.com/blogs/m....achine-learning/impr

Enhance conversational AI with advanced routing techniques with Amazon Bedrock Conversational artificial intelligence (AI) assistants are engineered to provide precise, real-time responses through intelligent routing of queries to the most suitable AI functions. With AWS generative AI services like Amazon Bedrock, developers can create systems that expertly manage and respond to user requests. Amazon Bedrock is a fully managed service that offers a choice of […]
https://aws.amazon.com/blogs/m....achine-learning/enha

Amazon Bedrock Update Wave: Expanded Model Library and Custom Model Support Amazon Bedrock, Amazon Web Services' relatively new and growing AI development platform, grew even more this week.
https://awsinsider.net/Article....s/2024/04/23/Amazon-

Accelerate ML workflows with Amazon SageMaker Studio Local Mode and Docker support We are excited to announce two new capabilities in Amazon SageMaker Studio that will accelerate iterative development for machine learning (ML) practitioners: Local Mode and Docker support. ML model development often involves slow iteration cycles as developers switch between coding, training, and deployment. Each step requires waiting for remote compute resources to start up, which […]
https://aws.amazon.com/blogs/m....achine-learning/acce