Favorite Model tuning is the experimental process of finding the optimal parameters and configurations for a machine learning (ML) model that result in the best possible desired outcome with a validation dataset. Single objective optimization with a performance metric is the most common approach for tuning ML models. However, in

Read More
Shared by AWS Machine Learning February 28, 2023

Favorite Amazon SageMaker multi-model endpoints (MMEs) provide a scalable and cost-effective way to deploy a large number of machine learning (ML) models. It gives you the ability to deploy multiple ML models in a single serving container behind a single endpoint. From there, SageMaker manages loading and unloading the models

Read More
Shared by AWS Machine Learning February 25, 2023

Favorite In recent years, advances in computer vision have enabled researchers, first responders, and governments to tackle the challenging problem of processing global satellite imagery to understand our planet and our impact on it. AWS recently released Amazon SageMaker geospatial capabilities to provide you with satellite imagery and geospatial state-of-the-art

Read More
Shared by AWS Machine Learning February 25, 2023

Favorite Over the last 10 years, a number of players have developed autonomous vehicle (AV) systems using deep neural networks (DNNs). These systems have evolved from simple rule-based systems to Advanced Driver Assistance Systems (ADAS) and fully autonomous vehicles. These systems require petabytes of data and thousands of compute units

Read More
Shared by AWS Machine Learning February 24, 2023

Favorite This post is co-written with Swagata Ashwani, Senior Data Scientist at Boomi. Boomi is an enterprise-level software as a service (SaaS) independent software vendor (ISV) that creates developer enablement tooling for software engineers. These tools integrate via API into Boomi’s core service offering. In this post, we discuss how

Read More
Shared by AWS Machine Learning February 23, 2023

Favorite 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,

Read More
Shared by AWS Machine Learning February 22, 2023

Favorite 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

Read More
Shared by AWS Machine Learning February 22, 2023

Favorite 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 stunning art in just a few seconds. Although creating impressive images can

Read More
Shared by AWS Machine Learning February 21, 2023

Favorite 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

Read More
Shared by AWS Machine Learning February 17, 2023

Favorite 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

Read More
Shared by AWS Machine Learning February 17, 2023