AWS Open Source Blog

Category: Amazon Machine Learning

Creating a bridge between machine learning and quantum computing with PennyLane

In this post, Josh Izaac (Xanadu) and Eric Kessler (AWS) explain how the open source PennyLane project helps bridge the gap between the quantum computing and machine learning communities. Today, we are announcing that AWS is joining the steering council of the PennyLane open source project for variational quantum computing and quantum machine learning. Our […]

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Deploy fast.ai-trained PyTorch model in TorchServe and host in Amazon SageMaker inference endpoint

Over the past few years, fast.ai has become one of the most cutting-edge, open source, deep learning frameworks and the go-to choice for many machine learning use cases based on PyTorch. It has not only democratized deep learning and made it approachable to general audiences, but fast.ai has also become a role model on how […]

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Virtual GPU device plugin for inference workloads in Kubernetes

Machine learning (ML) has become a centerpiece for enterprise transformation. AWS provides a broad and deep set of ML capabilities for builders with all levels of expertise. Developers with no prior ML experience can seamlessly build sophisticated AI-driven applications using AWS AI services. Developers and data scientists can use Amazon SageMaker, a managed machine learning […]

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workflow: how to deploy TorchServe on an Amazon EKS cluster for inference, which will allow you to quickly deploy a pre-trained machine learning model as a scalable, fault-tolerant web-service for low latency inference

Running TorchServe on Amazon Elastic Kubernetes Service

This article was contributed by Josiah Davis, Charles Frenzel, and Chen Wu. TorchServe is a model serving library that makes it easy to deploy and manage PyTorch models at scale in production environments. TorchServe removes the heavy lifting of deploying and serving PyTorch models with Kubernetes. TorchServe is built and maintained by AWS in collaboration […]

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How Amazon retail systems run machine learning predictions with Apache Spark using Deep Java Library

Today more and more companies are taking a personalized approach to content and marketing. For example, retailers are personalizing product recommendations and promotions for customers. An important step toward providing personalized recommendations is to identify a customer’s propensity to take action for a certain category. This propensity is based on a customer’s preferences and past […]

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Deploy machine learning models to Amazon SageMaker using the ezsmdeploy Python package and a few lines of code

Customers on AWS deploy trained machine learning (ML) and deep learning (DL) models in production using Amazon SageMaker, and using other services such as AWS Lambda, AWS Fargate, AWS Elastic Beanstalk, and Amazon Elastic Compute Cloud (Amazon EC2) to name a few. Amazon SageMaker provides SDKs and a console-only workflow to deploy trained models, and […]

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Adopting machine learning in your microservices with DJL (Deep Java Library) and Spring Boot

Many AWS customers—startups and large enterprises—are on a path to adopt machine learning and deep learning in their existing applications. The reasons for machine learning adoption are dictated by the pace of innovation in the industry, with business use cases ranging from customer service (including object detection from images and video streams, sentiment analysis) to […]

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AutoGluon how-to tutorial

Machine learning with AutoGluon, an open source AutoML library

If you work in data science, you might think that the hardest thing about machine learning is not knowing when you’ll be done. You start with a problem, a dataset, and an idea about how to solve it, but you never know whether your approach is going to work until later, after you’ve wasted time. […]

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diagram of host machine, container, code, and datasets and checkpoints

Why use Docker containers for machine learning development?

I like prototyping on my laptop, as much as the next person. When I want to collaborate, I push my code to GitHub and invite collaborators. And when I want to run experiments and need more compute power, I rent CPU and GPU instances in the cloud, copy my code and dependencies over, and run […]

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Andy Jassy giving the 2019 re:Invent keynote.

re:Cap part one – open source at re:Invent 2019

As the dust settles after another re:Invent closes, I wanted to put together a quick summary of all the open source-related announcements that happened in the run up to this year’s re:Invent and the week itself. If you are interested in open source in mobile web development, devops, containers, security, big data and data analytics, […]

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