AWS Open Source Blog

Category: Artificial Intelligence

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How to use InfluxDB and Grafana to visualize ML output with AWS IoT Greengrass

Machine learning (ML) algorithms are widely used for computer vision (CV) applications, such as image classification, object detection, and semantic segmentation. With the latest development of the Industrial Internet of Things (IIoT), ML algorithms can be directly implemented at the edge device to process image data and perform anomaly detection, such as for product quality […]

Enhancing data science environments with Vim, tmux, and Zsh on Amazon EC2

This post was written by Josiah Davis, Yin Song, and Anne Hu. The solution can also be found on GitHub. Many professional data scientists are adopting open source software development tools such as Vim, tmux, and Zsh to get more productivity out of their working environment. Vim is a free and open source, highly configurable […]

AWS DeepRacer is now open source and ready to hit the road with ROS 2

Reinforcement learning (RL) has become one of the most popular machine learning techniques for training robots in simulation. RL enables models to learn complex behaviors without labeled training data and allows the models to make short-term decisions while optimizing for longer-term goals. AWS DeepRacer offers an autonomous 1/18th scale race car driven by a reinforcement […]

Using Streamlit to build an interactive dashboard for data analysis on AWS

In this article, we’ll show how to stand up an Exploratory Data Analysis (EDA) dashboard for business users using Amazon Web Services (AWS) with Streamlit. Streamlit is an open source framework for data scientists to efficiently create interactive web-based data applications in pure Python. In this tutorial, the EDA dashboard allows for quick end-to-end deployment […]

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Build, train, and deploy Amazon Lookout for Vision models using the Python SDK

Amazon Lookout for Vision is a new machine learning (ML) service that spots defects and anomalies in visual representations using computer vision (CV). It was made available in Preview at AWS re:Invent 2020 and became generally available in February 2021. This service lets manufacturing companies increase quality and reduce operational costs by quickly identifying differences […]

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 […]

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 […]

Using Kedro pipelines to train Amazon SageMaker models

Machine learning (ML) and artificial intelligence (AI) adoption is growing at nearly 25 percent per year in a variety of businesses, which results in data scientists and engineers building more analytical models per person with similar levels of resources as last year. To keep up with such high demand, builders need to remove manual and […]

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 […]