AWS Open Source Blog

Category: Amazon SageMaker

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Twin Neural Network Training with PyTorch and Fast.ai and its Deployment with TorchServe on Amazon SageMaker

In this post we demonstrate how to train a Twin Neural Network based on PyTorch and Fast.ai, and deploy it with TorchServe on Amazon SageMaker inference endpoint. For demonstration purposes, we build an interactive web application for users to upload images and make inferences from the trained and deployed model, based on Streamlit, which is an open source framework for data scientists to efficiently create interactive web-based data applications in pure Python.

Scaling AI and Machine Learning Workloads with Ray on AWS

Scaling AI and Machine Learning Workloads with Ray on AWS

Learn how AWS contributes to the scalability and operational efficiency of open source Ray and how AWS customers use Ray with AWS-managed services for secure, scalable, and enterprise-ready workloads across the entire data processing and AI/ML pipeline.

Build, train, and deploy Amazon Fraud Detector models using the open source Python SDK

Companies providing digital services are looking for ways to effectively identify fraudulent activities, such as online payment fraud and fake account creation. Amazon Fraud Detector is a fully managed service that uses machine learning (ML) and builds on 20 years of fraud detection expertise from Amazon Web Services (AWS) and Amazon.com to automatically identify potentially […]

Implementing a hub and spoke dashboard for multi-account data science projects

Modern data science environments often involve many independent projects, each spanning multiple accounts. In order to maintain a global overview of the activities within the projects, a mechanism to collect data from the different accounts into a central one is crucial. In this post, we show how to leverage existing services—Amazon DynamoDB, AWS Lambda, Amazon […]

Solving the Traveling Salesperson Problem with deep reinforcement learning on Amazon SageMaker

The Traveling Salesperson Problem (TSP) is one of the most popular NP-hard combinatorial problems in the theoretical computer science and operations research (OR) community. It asks the following question: “Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and […]

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 […]

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 […]

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 […]