AWS Machine Learning Blog

Today, we are excited to announce the simplified Quick setup experience in Amazon SageMaker. With this new capability, individual users can launch Amazon SageMaker Studio with default presets in minutes. SageMaker Studio is an integrated development environment (IDE) for machine learning (ML). ML practitioners can perform all ML development steps—from preparing their data to building, […]

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.

In this post, we share how SageMaker facilitates the data science team at Scalable to manage the lifecycle of a data science project efficiently, namely the email classifier project. The lifecycle starts with the initial phase of data analysis and exploration with SageMaker Studio; moves on to model experimentation and deployment with SageMaker training, inference, and Hugging Face DLCs; and completes with a training pipeline with SageMaker Pipelines integrated with other AWS services

Today, we are excited to announce that the Falcon 180B foundation model developed by Technology Innovation Institute (TII) is available for customers through Amazon SageMaker JumpStart to deploy with one-click for running inference. With a 180-billion-parameter size and trained on a massive 3.5-trillion-token dataset, Falcon 180B is the largest and one of the most performant models with openly accessible weights. You can try out this model with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. In this post, we walk through how to discover and deploy the Falcon 180B model via SageMaker JumpStart.

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 […]

In this post, we’ll take you on a journey to rapidly build and deploy a document search indexing solution that helps your organization to better harness and extract insights from documents. Whether you’re in Human Resources looking for specific clauses in employee contracts, or a financial analyst sifting through a mountain of invoices to extract payment data, this solution is tailored to empower you to access the information you need with unprecedented speed and accuracy.

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.

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.