AI

Evaluate models with the Amazon Nova evaluation container using Amazon SageMaker AI

This blog post introduces the new Amazon Nova model evaluation features in Amazon SageMaker AI. This release adds custom metrics support, LLM-based preference testing, log probability capture, metadata analysis, and multi-node scaling for large evaluations. The new features include: Custom metrics use the bring your own metrics (BYOM) functions to control evaluation criteria for your […]

Evaluate models with the Amazon Nova evaluation container using Amazon SageMaker AI Read More »

Beyond the technology: Workforce changes for AI

Workplaces are increasingly integrating AI tools into daily operations, with AI assistants supporting teams, predictive analytics informing strategies, and automation streamlining workflows. AI has moved from experimental technology to standard business practice, changing how work gets done. Organizations need to understand what AI can do and how it affects their workforce to implement it successfully.

Beyond the technology: Workforce changes for AI Read More »

Enhanced performance for Amazon Bedrock Custom Model Import

You can now achieve significant performance improvements when using Amazon Bedrock Custom Model Import, with reduced end-to-end latency, faster time-to-first-token, and improved throughput through advanced PyTorch compilation and CUDA graph optimizations. With Amazon Bedrock Custom Model Import you can to bring your own foundation models to Amazon Bedrock for deployment and inference at scale. These

Enhanced performance for Amazon Bedrock Custom Model Import Read More »

Researchers discover a shortcoming that makes LLMs less reliable

Large language models (LLMs) sometimes learn the wrong lessons, according to an MIT study. Rather than answering a query based on domain knowledge, an LLM could respond by leveraging grammatical patterns it learned during training. This can cause a model to fail unexpectedly when deployed on new tasks. The researchers found that models can mistakenly

Researchers discover a shortcoming that makes LLMs less reliable Read More »

Amazon SageMaker AI introduces EAGLE based adaptive speculative decoding to accelerate generative AI inference

Generative AI models continue to expand in scale and capability, increasing the demand for faster and more efficient inference. Applications need low latency and consistent performance without compromising output quality. Amazon SageMaker AI introduces new enhancements to its inference optimization toolkit that bring EAGLE based adaptive speculative decoding to more model architectures. These updates make

Amazon SageMaker AI introduces EAGLE based adaptive speculative decoding to accelerate generative AI inference Read More »

Train custom computer vision defect detection model using Amazon SageMaker

On October 10, 2024, Amazon announced the discontinuation of the Amazon Lookout for Vision service, with a scheduled shut down date of October 31, 2025 (see Exploring alternatives and seamlessly migrating data from Amazon Lookout for Vision blog post). As part of our transition guidance for customers, we recommend the use of Amazon SageMaker AI tools

Train custom computer vision defect detection model using Amazon SageMaker Read More »

MIT scientists debut a generative AI model that could create molecules addressing hard-to-treat diseases

More than 300 people across academia and industry spilled into an auditorium to attend a BoltzGen seminar on Thursday, Oct. 30, hosted by the Abdul Latif Jameel Clinic for Machine Learning in Health (MIT Jameel Clinic). Headlining the event was MIT PhD student and BoltzGen’s first author Hannes Stärk, who had announced BoltzGen just a few days

MIT scientists debut a generative AI model that could create molecules addressing hard-to-treat diseases Read More »