AWS Machine Learning Blog

Category: Amazon Machine Learning

Try asking OSU-OKC bot, OKC Pete, some questions of your own via the university website.

Building an omnichannel Q&A chatbot with Amazon Connect, Amazon Lex, Amazon Kendra, and the open-source QnABot project

For many students, embarking on a higher education journey is an exciting time filled with new experiences. However, like anything new, it also can also bring plenty of questions to answer and obstacles to overcome. Oklahoma State University, Oklahoma City (OSU-OKC) recognized this, and was intent on providing a better solution to address student questions […]

Data processing options for AI/ML

This blog post was reviewed and updated June, 2022 to include new features that have been added to the Data processing such as Amazon SageMaker Studio and EMR integration. Training an accurate machine learning (ML) model requires many different steps, but none are potentially more important than data processing. Examples of processing steps include converting […]

Using container images to run PyTorch models in AWS Lambda

July 2024: This post was reviewed for accuracy. PyTorch is an open-source machine learning (ML) library widely used to develop neural networks and ML models. Those models are usually trained on multiple GPU instances to speed up training, resulting in expensive training time and model sizes up to a few gigabytes. After they’re trained, these […]

The following diagram is the architecture for the secure environment developed in this workshop.

Building secure machine learning environments with Amazon SageMaker

As businesses and IT leaders look to accelerate the adoption of machine learning (ML) and artificial intelligence (AI), there is a growing need to understand how to build secure and compliant ML environments that meet enterprise requirements. One major challenge you may face is integrating ML workflows into existing IT and business work streams. A […]

Greg Baker at his work station.

Learn from the winner of the AWS DeepComposer Chartbusters Track or Treat challenge

AWS is excited to announce the winner of the AWS DeepComposer Chartbusters Track or Treat challenge, Greg Baker. AWS DeepComposer gives developers a creative way to get started with machine learning (ML). In June 2020, we launched Chartbusters, a global competition in which developers use AWS DeepComposer to create original AI-generated compositions and compete to […]

Amazon DevOps Guru is powered by pre-trained ML models that encode operational excellence

On December 1, 2020, we announced the preview of Amazon DevOps Guru, a machine learning (ML)-powered service that gives operators of cloud-based applications a simpler way to measure and improve an application’s operational performance and availability to reduce expensive downtime. Amazon DevOps Guru is a turn-key solution that helps operators by automatically ingesting operational data […]

Using the AWS DeepRacer new Soft Actor Critic algorithm with continuous action spaces

AWS DeepRacer is the fastest way to get started with machine learning (ML). You can train reinforcement learning (RL) models by using a 1/18th scale autonomous vehicle in a cloud-based virtual simulator and compete for prizes and glory in the global AWS DeepRacer League. We’re excited to bring you two new features available on the […]

This month in AWS Machine Learning: January edition

Hello and welcome to our first “This month in AWS Machine Learning” of 2021! Every day there is something new going on in the world of AWS Machine Learning—from launches to new to use cases to interactive trainings. We’re packaging some of the not-to-miss information from the ML Blog and beyond for easy perusing each […]

The following diagram illustrates the architecture for our experiments.

Building predictive disease models using Amazon SageMaker with Amazon HealthLake normalized data

In this post, we walk you through the steps to build machine learning (ML) models in Amazon SageMaker with data stored in Amazon HealthLake using two example predictive disease models we trained on sample data using the MIMIC-III dataset. This dataset was developed by the MIT lab for Computational Physiology and consists of de-identified healthcare […]

Building and deploying an object detection computer vision application at the edge with AWS Panorama

Computer vision (CV) is sought after technology among companies looking to take advantage of machine learning (ML) to improve their business processes. Enterprises have access to large amounts of video assets from their existing cameras, but the data remains largely untapped without the right tools to gain insights from it. CV provides the tools to […]