AWS Machine Learning Blog

Machine learning (ML) is transforming every industry, process, and business, but the path to success is not always straightforward. In this blog post, we demonstrate how Duke Energy, a Fortune 150 company headquartered in Charlotte, NC., collaborated with the AWS Machine Learning Solutions Lab (MLSL) to use computer vision to automate the inspection of wooden utility poles and help prevent power outages, property damage and even injuries.

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.

This post details how to set up container-based GPU metrics and provides an example of collecting these metrics from EKS pods.

In this post, we provide some best practices to maximize the value of SageMaker Pipelines and make the development experience seamless. We also discuss some common design scenarios and patterns when building SageMaker Pipelines and provide examples for addressing them.

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 shows you how to configure the Amazon Kendra AEM connector to index your content and search your AEM assets and pages. The connector also ingests the access control list (ACL) information for each document. The ACL information is used to show search results filtered by what a user has access to.

Today, we are excited to announce the capability to fine-tune Llama 2 models by Meta using Amazon SageMaker JumpStart. The Llama 2 family of large language models (LLMs) is a collection of pre-trained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Fine-tuned LLMs, called Llama-2-chat, are optimized for dialogue use cases.

In this post, we introduce a novel method to perform content moderation on image data with multi-modal pre-training and a large language model (LLM). With multi-modal pre-training, we can directly query the image content based on a set of questions of interest and the model will be able to answer these questions. This enables users to chat with the image to confirm if it contains any inappropriate content that violates the organization’s policies. We use the powerful generating capability of LLMs to generate the final decision including safe/unsafe labels and category type. In addition, by designing a prompt, we can make an LLM generate the defined output format, such as JSON format. The designed prompt template allows the LLM to determine if the image violates the moderation policy, identify the category of violation, explain why, and provide the output in a structured JSON format.

In this post, we show how the Carrier and AWS teams applied ML to predict faults across large fleets of equipment using a single model. We first highlight how we use AWS Glue for highly parallel data processing. We then discuss how Amazon SageMaker helps us with feature engineering and building a scalable supervised deep learning model.

In this post, we target these situations and solve the problem of risking high costs by deploying large foundation models to Amazon SageMaker asynchronous endpoints from Amazon SageMaker JumpStart. This can help cut costs of the architecture, allowing the endpoint to run only when requests are in the queue and for a short time-to-live, while scaling down to zero when no requests are waiting to be serviced. This sounds great for a lot of use cases; however, an endpoint that has scaled down to zero will introduce a cold start time before being able to serve inferences.