AWS Machine Learning Blog

Category: Amazon SageMaker

Prepare data for predicting credit risk using Amazon SageMaker Data Wrangler and Amazon SageMaker Clarify

For data scientists and machine learning (ML) developers, data preparation is one of the most challenging and time-consuming tasks of building ML solutions. In an often iterative and highly manual process, data must be sourced, analyzed, cleaned, and enriched before it can be used to train an ML model. Typical tasks associated with data preparation […]

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Maximize TensorFlow performance on Amazon SageMaker endpoints for real-time inference

Machine learning (ML) is realized in inference. The business problem you want your ML model to solve is the inferences or predictions that you want your model to generate. Deployment is the stage in which a model, after being trained, is ready to accept inference requests. In this post, we describe the parameters that you […]

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Build a scalable machine learning pipeline for ultra-high resolution medical images using Amazon SageMaker

Neural networks have proven effective at solving complex computer vision tasks such as object detection, image similarity, and classification. With the evolution of low-cost GPUs, the computational cost of building and deploying a neural network has drastically reduced. However, most techniques are designed to handle pixel resolutions commonly found in visual media. For example, typical […]

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How Genworth built a serverless ML pipeline on AWS using Amazon SageMaker and AWS Glue

This post is co-written with Liam Pearson, a Data Scientist at Genworth Mortgage Insurance Australia Limited. Genworth Mortgage Insurance Australia Limited is a leading provider of lenders mortgage insurance (LMI) in Australia; their shares are traded on Australian Stock Exchange as ASX: GMA. Genworth Mortgage Insurance Australia Limited is a lenders mortgage insurer with over […]

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Creating an end-to-end application for orchestrating custom deep learning HPO, training, and inference using AWS Step Functions

Amazon SageMaker hyperparameter tuning provides a built-in solution for scalable training and hyperparameter optimization (HPO). However, for some applications (such as those with a preference of different HPO libraries or customized HPO features), we need custom machine learning (ML) solutions that allow retraining and HPO. This post offers a step-by-step guide to build a custom deep […]

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Build a medical sentence matching application using BERT and Amazon SageMaker

Determining the relevance of a sentence when compared to a specific document is essential for many different types of applications across various industries. In this post, we focus on a use case within the healthcare field to help determine the accuracy of information regarding patient health. Frequently, during each patient visit, a new document is […]

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Securing Amazon SageMaker Studio internet traffic using AWS Network Firewall

Amazon SageMaker Studio is a web-based fully integrated development environment (IDE) where you can perform end-to-end machine learning (ML) development to prepare data and build, train, and deploy models. Like other AWS services, Studio supports a rich set of security-related features that allow you to build highly secure and compliant environments. One of these fundamental […]

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It’s here! Join us for Amazon SageMaker Month, 30 days of content, discussion, and news

Want to accelerate machine learning (ML) innovation in your organization? Join us for 30 days of new Amazon SageMaker content designed to help you build, train, and deploy ML models faster. On April 20, we’re kicking off 30 days of hands-on workshops, Twitch sessions, Slack chats, and partner perspectives. Our goal is to connect you […]

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Estimating 3D pose for athlete tracking using 2D videos and Amazon SageMaker Studio

In preparation for the upcoming Olympic Games, Intel®, an American multinational corporation and one of the world’s largest technology companies, developed a concept around 3D Athlete Tracking (3DAT). 3DAT is a machine learning (ML) solution to create real-time digital models of athletes in competition in order to increase fan engagement during broadcasts. Intel was looking […]

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Implement checkpointing with TensorFlow for Amazon SageMaker Managed Spot Training

Customers often ask us how can they lower their costs when conducting deep learning training on AWS. Training deep learning models with libraries such as TensorFlow, PyTorch, and Apache MXNet usually requires access to GPU instances, which are AWS instances types that provide access to NVIDIA GPUs with thousands of compute cores. GPU instance types […]

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