AWS Machine Learning Blog

Improving your LLMs with RLHF on Amazon SageMaker

In this blog post, we illustrate how RLHF can be performed on Amazon SageMaker by conducting an experiment with the popular, open-sourced RLHF repo Trlx. Through our experiment, we demonstrate how RLHF can be used to increase the helpfulness or harmlessness of a large language model using the publicly available Helpfulness and Harmlessness (HH) dataset provided by Anthropic. Using this dataset, we conduct our experiment with Amazon SageMaker Studio notebook that is running on an ml.p4d.24xlarge instance. Finally, we provide a Jupyter notebook to replicate our experiments.

Dataset architecture

How United Airlines built a cost-efficient Optical Character Recognition active learning pipeline

In this post, we discuss how United Airlines, in collaboration with the Amazon Machine Learning Solutions Lab, build an active learning framework on AWS to automate the processing of passenger documents. “In order to deliver the best flying experience for our passengers and make our internal business process as efficient as possible, we have developed […]

Optimize generative AI workloads for environmental sustainability

To add to our guidance for optimizing deep learning workloads for sustainability on AWS, this post provides recommendations that are specific to generative AI workloads. In particular, we provide practical best practices for different customization scenarios, including training models from scratch, fine-tuning with additional data using full or parameter-efficient techniques, Retrieval Augmented Generation (RAG), and prompt engineering.

Train and deploy ML models in a multicloud environment using Amazon SageMaker

In this post, we demonstrate one of the many options that you have to take advantage of AWS’s broadest and deepest set of AI/ML capabilities in a multicloud environment. We show how you can build and train an ML model in AWS and deploy the model in another platform. We train the model using Amazon SageMaker, store the model artifacts in Amazon Simple Storage Service (Amazon S3), and deploy and run the model in Azure.

Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets

Multi-modal data is a valuable component of the financial industry, encompassing market, economic, customer, news and social media, and risk data. Financial organizations generate, collect, and use this data to gain insights into financial operations, make better decisions, and improve performance. However, there are challenges associated with multi-modal data due to the complexity and lack […]

How VirtuSwap accelerates their pandas-based trading simulations with an Amazon SageMaker Studio custom container and AWS GPU instances

This post is written in collaboration with Dima Zadorozhny and Fuad Babaev from VirtuSwap. VirtuSwap is a startup company developing innovative technology for decentralized exchange of assets on blockchains. VirtuSwap’s technology provides more efficient trading for assets that don’t have a direct pair between them. The absence of a direct pair leads to costly indirect trading, […]

Unlock ML insights using the Amazon SageMaker Feature Store Feature Processor

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. Feature quality is critical to ensure a highly accurate ML model. […]

Orchestrate Ray-based machine learning workflows using Amazon SageMaker

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. Although this enables parallelization of tasks across multiple nodes, leading to accelerated training times, enhanced scalability, and improved […]

Designing resilient cities at Arup using Amazon SageMaker geospatial capabilities

This post is co-authored with Richard Alexander and Mark Hallows from Arup. Arup is a global collective of designers, consultants, and experts dedicated to sustainable development. Data underpins Arup consultancy for clients with world-class collection and analysis providing insight to make an impact. The solution presented here is to direct decision-making processes for resilient city […]

Solution Overview

Learn how to build and deploy tool-using LLM agents using AWS SageMaker JumpStart Foundation Models

Large language model (LLM) agents are programs that extend the capabilities of standalone LLMs with 1) access to external tools (APIs, functions, webhooks, plugins, and so on), and 2) the ability to plan and execute tasks in a self-directed fashion. Often, LLMs need to interact with other software, databases, or APIs to accomplish complex tasks. […]