AWS Machine Learning Blog

Category: Artificial Intelligence

AWS Celebrates 5 Years of Innovation with Amazon SageMaker

In just 5 years, tens of thousands of customers have tapped Amazon SageMaker to create millions of models, train models with billions of parameters, and generate hundreds of billions of monthly predictions. The seeds of a machine learning (ML) paradigm shift were there for decades, but with the ready availability of virtually infinite compute capacity, […]

Run inference at scale for OpenFold, a PyTorch-based protein folding ML model, using Amazon EKS

This post was co-written with Sachin Kadyan, a leading developer of OpenFold. In drug discovery, understanding the 3D structure of proteins is key to assessing the ability of a drug to bind to it, directly impacting its efficacy. Predicting the 3D protein form, however, is very complex, challenging, expensive, and time consuming, and can take […]

Configure DTMF slots and ordered retry prompts with Amazon Lex

This post walks you through a few new features that make it simple to design a conversational flow entirely within Amazon Lex that adheres to best practices for IVR design related to retry prompting. We also cover how to configure a DTMF-only prompt as well as other attributes like timeouts and barge-in. When designing an […]

Run multiple deep learning models on GPU with Amazon SageMaker multi-model endpoints

As AI adoption is accelerating across the industry, customers are building sophisticated models that take advantage of new scientific breakthroughs in deep learning. These next-generation models allow you to achieve state-of-the-art, human-like performance in the fields of natural language processing (NLP), computer vision, speech recognition, medical research, cybersecurity, protein structure prediction, and many others. For […]

Detect patterns in text data with Amazon SageMaker Data Wrangler

In this post, we introduce a new analysis in the Data Quality and Insights Report of Amazon SageMaker Data Wrangler. This analysis assists you in validating textual features for correctness and uncovering invalid rows for repair or omission. Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from […]

Reduce deep learning training time and cost with MosaicML Composer on AWS

In the past decade, we have seen Deep learning (DL) science adopted at a tremendous pace by AWS customers. The plentiful and jointly trained parameters of DL models have a large representational capacity that brought improvements in numerous customer use cases, including image and speech analysis, natural language processing (NLP), time series processing, and more. […]

Create synthetic data for computer vision pipelines on AWS

Collecting and annotating image data is one of the most resource-intensive tasks on any computer vision project. It can take months at a time to fully collect, analyze, and experiment with image streams at the level you need in order to compete in the current marketplace. Even after you’ve successfully collected data, you still have […]

To better illustrate the changes, the following figure displays both a standard MLOps pipeline created automatically by SageMaker (Steps 1-5) as well as changes required to extend it to a secondary Region (Steps 6-11).

Enable CI/CD of multi-Region Amazon SageMaker endpoints

Amazon SageMaker and SageMaker inference endpoints provide a capability of training and deploying your AI and machine learning (ML) workloads. With inference endpoints, you can deploy your models for real-time or batch inference. The endpoints support various types of ML models hosted using AWS Deep Learning Containers or your own containers with custom AI/ML algorithms. […]

Detect fraudulent transactions using machine learning with Amazon SageMaker

Businesses can lose billions of dollars each year due to malicious users and fraudulent transactions. As more and more business operations move online, fraud and abuses in online systems are also on the rise. To combat online fraud, many businesses have been using rule-based fraud detection systems. However, traditional fraud detection systems rely on a […]

Implement RStudio on your AWS environment and access your data lake using AWS Lake Formation permissions

R is a popular analytic programming language used by data scientists and analysts to perform data processing, conduct statistical analyses, create data visualizations, and build machine learning (ML) models. RStudio, the integrated development environment for R, provides open-source tools and enterprise-ready professional software for teams to develop and share their work across their organization building, […]