AWS Machine Learning Blog

How iFood built a platform to run hundreds of machine learning models with Amazon SageMaker Inference

In this post, we show how iFood uses SageMaker to revolutionize its ML operations. By harnessing the power of SageMaker, iFood streamlines the entire ML lifecycle, from model training to deployment. This integration not only simplifies complex processes but also automates critical tasks.

Build an enterprise synthetic data strategy using Amazon Bedrock

In this post, we explore how to use Amazon Bedrock for synthetic data generation, considering these challenges alongside the potential benefits to develop effective strategies for various applications across multiple industries, including AI and machine learning (ML).

Llama 4 family of models from Meta are now available in SageMaker JumpStart

Today, we’re excited to announce the availability of Llama 4 Scout and Maverick models in Amazon SageMaker JumpStart. In this blog post, we walk you through how to deploy and prompt a Llama-4-Scout-17B-16E-Instruct model using SageMaker JumpStart.

Effectively use prompt caching on Amazon Bedrock

Prompt caching, now generally available on Amazon Bedrock with Anthropic’s Claude 3.5 Haiku and Claude 3.7 Sonnet, along with Nova Micro, Nova Lite, and Nova Pro models, lowers response latency by up to 85% and reduces costs up to 90% by caching frequently used prompts across multiple API calls. This post provides a detailed overview of the prompt caching feature on Amazon Bedrock and offers guidance on how to effectively use this feature to achieve improved latency and cost savings.

Advanced tracing and evaluation of generative AI agents using LangChain and Amazon SageMaker AI MLFlow

In this post, I show you how to combine LangChain’s LangGraph, Amazon SageMaker AI, and MLflow to demonstrate a powerful workflow for developing, evaluating, and deploying sophisticated generative AI agents. This integration provides the tools needed to gain deep insights into the generative AI agent’s performance, iterate quickly, and maintain version control throughout the development process.

Evaluate models or RAG systems using Amazon Bedrock Evaluations – Now generally available

Today, we’re excited to announce the general availability of these evaluation features in Amazon Bedrock Evaluations, along with significant enhancements that make them fully environment-agnostic. In this post, we explore these new features in detail, showing you how to evaluate both RAG systems and models with practical examples. We demonstrate how to use the comparison capabilities to benchmark different implementations and make data-driven decisions about your AI deployments.

Reinforcement learning from human feedback (RLHF) vs. AI feedback (RLAIF)

Fine-tune large language models with reinforcement learning from human or AI feedback

In this post, we introduce a state-of-the-art method to fine-tune LLMs by reinforcement learning, reviewed the pros and cons of RLHF vs. RLAIF vs. DPO, and saw how to scale LLM fine-tuning efforts with RLAIF. We also see how to implement an end-to-end RLAIF pipeline on SageMaker using the Hugging Face Transformer and TRL libraries, and using either off-the-shelf toxicity reward models to align responses during PPO or by directly prompting an LLM to generate quantitative reward feedback during PPO.

How Lumi streamlines loan approvals with Amazon SageMaker AI

Lumi is a leading Australian fintech lender empowering small businesses with fast, flexible, and transparent funding solutions. They use real-time data and machine learning (ML) to offer customized loans that fuel sustainable growth and solve the challenges of accessing capital. This post explores how Lumi uses Amazon SageMaker AI to meet this goal, enhance their transaction processing and classification capabilities, and ultimately grow their business by providing faster processing of loan applications, more accurate credit decisions, and improved customer experience.

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