Come Partner with Us

Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

Favorite Evaluating large language models (LLMs) is crucial as LLM-based systems become increasingly powerful and relevant in our society. Rigorous testing allows us to understand an LLM’s capabilities, limitations, and potential biases, and provide actionable feedback to identify and mitigate risk. Furthermore, evaluation processes are important not only for LLMs,

Read More
Shared by AWS Machine Learning January 29, 2025

Optimizing AI responsiveness: A practical guide to Amazon Bedrock latency-optimized inference

Favorite In production generative AI applications, responsiveness is just as important as the intelligence behind the model. Whether it’s customer service teams handling time-sensitive inquiries or developers needing instant code suggestions, every second of delay, known as latency, can have a significant impact. As businesses increasingly use large language models

Read More
Shared by AWS Machine Learning January 29, 2025

Secure a generative AI assistant with OWASP Top 10 mitigation

Favorite A common use case with generative AI that we usually see customers evaluate for a production use case is a generative AI-powered assistant. However, before it can be deployed, there is the typical production readiness assessment that includes concerns such as understanding the security posture, monitoring and logging, cost

Read More
Shared by AWS Machine Learning January 25, 2025