Scale LLM fine-tuning with Hugging Face and Amazon SageMaker AI

Favorite Enterprises are increasingly shifting from relying solely on large, general-purpose language models to developing specialized large language models (LLMs) fine-tuned on their own proprietary data. Although foundation models (FMs) offer impressive general capabilities, they often fall short when applied to the complexities of enterprise environments—where accuracy, security, compliance, and

Read More
Shared by AWS Machine Learning February 10, 2026

Evaluate generative AI models with an Amazon Nova rubric-based LLM judge on Amazon SageMaker AI (Part 2)

Favorite In the post Evaluating generative AI models with Amazon Nova LLM-as-a-Judge on Amazon SageMaker AI, we introduced the Amazon Nova LLM-as-a-judge capability, which is a specialized evaluation model available through Amazon SageMaker AI that you can use to systematically measure the relative performance of generative AI systems. SageMaker AI

Read More
Shared by AWS Machine Learning February 7, 2026

Manage Amazon SageMaker HyperPod clusters using the HyperPod CLI and SDK

Favorite Training and deploying large AI models requires advanced distributed computing capabilities, but managing these distributed systems shouldn’t be complex for data scientists and machine learning (ML) practitioners. The command line interface (CLI) and software development kit (SDK) for Amazon SageMaker HyperPod with Amazon Elastic Kubernetes Service (Amazon EKS) orchestration simplify

Read More
Shared by AWS Machine Learning February 7, 2026

Structured outputs on Amazon Bedrock: Schema-compliant AI responses

Favorite Today, we’re announcing structured outputs on Amazon Bedrock—a capability that fundamentally transforms how you can obtain validated JSON responses from foundation models through constrained decoding for schema compliance. This represents a paradigm shift in AI application development. Instead of validating JSON responses and writing fallback logic for when they

Read More
Shared by AWS Machine Learning February 7, 2026

A practical guide to Amazon Nova Multimodal Embeddings

Favorite Embedding models power many modern applications—from semantic search and Retrieval-Augmented Generation (RAG) to recommendation systems and content understanding. However, selecting an embedding model requires careful consideration—after you’ve ingested your data, migrating to a different model means re-embedding your entire corpus, rebuilding vector indexes, and validating search quality from scratch.

Read More
Shared by AWS Machine Learning February 6, 2026

How Associa transforms document classification with the GenAI IDP Accelerator and Amazon Bedrock

Favorite This is a guest post co-written with David Meredith and Josh Zacharias from Associa. Associa, North America’s largest community management company, oversees approximately 7.5 million homeowners with 15,000 employees across more than 300 branch offices. The company manages approximately 48 million documents across 26 TB of data, but their

Read More
Shared by AWS Machine Learning February 6, 2026

Accelerating your marketing ideation with generative AI – Part 2: Generate custom marketing images from historical references

Favorite Marketing teams face major challenges creating campaigns in today’s digital environment. They must navigate through complex data analytics and rapidly changing consumer preferences to produce engaging, personalized content across multiple channels while maintaining brand consistency and working within tight deadlines. Using generative AI can streamline and accelerate the creative

Read More
Shared by AWS Machine Learning February 5, 2026

Agentic AI for healthcare data analysis with Amazon SageMaker Data Agent

Favorite Performing research and clinical analytics on vast amounts of clinical data can be difficult. Healthcare data scientists and epidemiologists possess deep domain expertise in patient care, disease patterns, and clinical outcomes, yet they often spend weeks navigating complex data infrastructures, writing boilerplate code, and wrestling with technical barriers before

Read More
Shared by AWS Machine Learning February 4, 2026

AI agents in enterprises: Best practices with Amazon Bedrock AgentCore

Favorite Building production-ready AI agents requires careful planning and execution across the entire development lifecycle. The difference between a prototype that impresses in a demo and an agent that delivers value in production is achieved through disciplined engineering practices, robust architecture, and continuous improvement. This post explores nine essential best

Read More
Shared by AWS Machine Learning February 4, 2026