AWS Machine Learning Blog

After you build, train, and evaluate your machine learning (ML) model to ensure it’s solving the intended business problem proposed, you want to deploy that model to enable decision-making in business operations. Models that support business-critical functions are deployed to a production environment where a model release strategy is put in place. Given the nature […]

We’re thrilled to announce an expanded collaboration between AWS and Hugging Face to accelerate the training, fine-tuning, and deployment of large language and vision models used to create generative AI applications. Generative AI applications can perform a variety of tasks, including text summarization, answering questions, code generation, image creation, and writing essays and articles. AWS […]

March 2023: This blog was reviewed and updated with AMT HPO support for finetuning text-to-image Stable Diffusion models. In November 2022, we announced that AWS customers can generate images from text with Stable Diffusion models in Amazon SageMaker JumpStart. Stable Diffusion is a deep learning model that allows you to generate realistic, high-quality images and […]

Modern model pre-training often calls for larger cluster deployment to reduce time and cost. At the server level, such training workloads demand faster compute and increased memory allocation. As models grow to hundreds of billions of parameters, they require a distributed training mechanism that spans multiple nodes (instances). In October 2022, we launched Amazon EC2 […]

Enterprises across the globe are looking to utilize multiple data sources to implement a unified search experience for their employees and end customers. Considering the large volume of data that needs to be examined and indexed, the retrieval speed, solution scalability, and search performance become key factors to consider when choosing an enterprise intelligent search […]

Amazon SageMaker JumpStart is the machine learning (ML) hub of SageMaker that offers over 350 built-in algorithms, pre-trained models, and pre-built solution templates to help you get started with ML fast. JumpStart provides one-click access to a wide variety of pre-trained models for common ML tasks such as object detection, text classification, summarization, text generation […]

Amazon Kendra is an intelligent search service powered by machine learning (ML). It indexes the documents stored in a wide range of repositories and finds the most relevant document based on the keywords or natural language questions the user has searched for. In some scenarios, you need the search results to be filtered based on […]

We’re excited to announce that Amazon Personalize now lets you measure how your personalized recommendations can help you achieve your business goals. After specifying the metrics that you want to track, you can identify which campaigns and recommenders are most impactful and understand the impact of recommendations on your business metrics. All customers want to […]

This post is co-written by Zdenko Estok, Cloud Architect at Accenture and Sakar Selimcan, DeepRacer SME at Accenture. With the increasing use of artificial intelligence (AI) and machine learning (ML) for a vast majority of industries (ranging from healthcare to insurance, from manufacturing to marketing), the primary focus shifts to efficiency when building and training […]

This post is co-written with Jonathan Jung, Mike Band, Michael Chi, and Thompson Bliss at the National Football League. A coverage scheme refers to the rules and responsibilities of each football defender tasked with stopping an offensive pass. It is at the core of understanding and analyzing any football defensive strategy. Classifying the coverage scheme […]