Category: Technical How-to

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. […]

In first part of this multi-series blog post, you will learn how to create a scalable training pipeline and prepare training data for Comprehend Custom Classification models. We will introduce a custom classifier training pipeline that can be deployed in your AWS account with few clicks.

This post addresses the challenge faced by developers and support teams when application logs are presented in languages other than English, making it difficult for them to debug and provide support. The proposed solution uses Amazon Translate to automatically translate non-English logs in CloudWatch, and provides step-by-step guidance on deploying the solution in your environment.

Amazon SageMaker Domain supports SageMaker machine learning (ML) environments, including SageMaker Studio and SageMaker Canvas. SageMaker Studio is a fully integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all ML development steps, from preparing data to building, training, and deploying your ML models, improving […]

Digital publishers are continuously looking for ways to streamline and automate their media workflows in order to generate and publish new content as rapidly as they can. Publishers can have repositories containing millions of images and in order to save money, they need to be able to reuse these images across articles. Finding the image that best matches an article in repositories of this scale can be a time-consuming, repetitive, manual task that can be automated. It also relies on the images in the repository being tagged correctly, which can also be automated (for a customer success story, refer to Aller Media Finds Success with KeyCore and AWS). In this post, we demonstrate how to use Amazon Rekognition, Amazon SageMaker JumpStart, and Amazon OpenSearch Service to solve this business problem.

In this post, we will build an end-to-end solution to find optimal control policies using only historical data on Amazon SageMaker using Ray’s RLlib library. To learn more about reinforcement learning, see Use Reinforcement Learning with Amazon SageMaker.

In this post, we build a secure enterprise application using AWS Amplify that invokes an Amazon SageMaker JumpStart foundation model, Amazon SageMaker endpoints, and Amazon OpenSearch Service to explain how to create text-to-text or text-to-image and Retrieval Augmented Generation (RAG). You can use this post as a reference to build secure enterprise applications in the Generative AI domain using AWS services.

This post demonstrates a strategy for fine-tuning publicly available LLMs for the task of radiology report summarization using AWS services. LLMs have demonstrated remarkable capabilities in natural language understanding and generation, serving as foundation models that can be adapted to various domains and tasks. There are significant benefits to using a pre-trained model. It reduces computation costs, reduces carbon footprints, and allows you to use state-of-the-art models without having to train one from scratch.

In this post, we describe how to create an MLOps workflow for batch inference that automates job scheduling, model monitoring, retraining, and registration, as well as error handling and notification by using Amazon SageMaker, Amazon EventBridge, AWS Lambda, Amazon Simple Notification Service (Amazon SNS), HashiCorp Terraform, and GitLab CI/CD. The presented MLOps workflow provides a reusable template for managing the ML lifecycle through automation, monitoring, auditability, and scalability, thereby reducing the complexities and costs of maintaining batch inference workloads in production.

In this post, we discuss how to implement federated learning on Amazon SageMaker to run ML with decentralized training data.