AWS Machine Learning Blog

To make machine learning (ML) more accessible, Amazon launched Amazon SageMaker Studio Lab at AWS re:Invent 2021. Today, tens of thousands of customers use it every day to learn and experiment with ML for free. We made it simple to get started with just an email address, without the need for installs, setups, credit cards, […]

This post was co-authored by Arun Gupta, the Director of Business Intelligence at Prodege, LLC. Prodege is a data-driven marketing and consumer insights platform comprised of consumer brands—Swagbucks, MyPoints, Tada, ySense, InboxDollars, InboxPounds, DailyRewards, PollFish, and Upromise—along with a complementary suite of business solutions for marketers and researchers. Prodege has 120 million users and has […]

Business analysts work with data and like to analyze, explore, and understand data to achieve effective business outcomes. To address business problems, they often rely on machine learning (ML) practitioners such as data scientists to assist with techniques such as utilizing ML to build models using existing data and generate predictions. However, it isn’t always […]

Medical imaging is an important tool for the diagnosis and localization of disease. Over the past decade, collections of medical images have grown rapidly, and open repositories such as The Cancer Imaging Archive and Imaging Data Commons have democratized access to this vast imaging data. Computational tools such as machine learning (ML) and artificial intelligence […]

Amazon SageMaker is a fully managed service that provides developers and data scientists the ability to quickly build, train, and deploy machine learning (ML) models. With SageMaker, you can deploy your ML models on hosted endpoints and get inference results in real time. You can easily view the performance metrics for your endpoints in Amazon […]

The COVID-19 global pandemic has accelerated the need to verify and onboard users online across several industries, such as financial services, insurance, and healthcare. When it comes to user experience it is crucial to provide a frictionless transaction while maintaining a high standard for identity verification.  The question is, how do you verify real people […]

Amazon SageMaker is a fully-managed service that provides every developer and data scientist with the ability to quickly build, train, and deploy machine learning (ML) models at scale. ML is realized in inference. SageMaker offers four Inference options: Real-Time Inference Serverless Inference Asynchronous Inference Batch Transform These four options can be broadly classified into Online […]

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and is composed of tumors with significant molecular heterogeneity resulting from differences in intrinsic oncogenic signaling pathways [1]. Enabling precision medicine, anticipating patient preferences, detecting disease, and improving care quality for NSCLC patients are important topics among healthcare and life sciences (HCLS) […]

Amazon SageMaker Data Wrangler is a capability of Amazon SageMaker that makes it faster for data scientists and engineers to prepare high-quality features for machine learning (ML) applications via a visual interface. Data Wrangler reduces the time it takes to aggregate and prepare data for ML from weeks to minutes. With Data Wrangler, you can […]

March 2023: This post was reviewed and updated with support for Stable Diffusion inpainting model. Today, we announce that Stable Diffusion 1 and Stable Diffusion 2 are available in Amazon SageMaker JumpStart. JumpStart is the machine learning (ML) hub of SageMaker that provides hundreds of built-in algorithms, pre-trained models, and end-to-end solution templates to help you quickly get started with […]