AWS Machine Learning Blog

Category: Amazon SageMaker

Automate multi-modality, parallel data labeling workflows with Amazon SageMaker Ground Truth and AWS Step Functions

This is the first in a two-part series on the Amazon SageMaker Ground Truth hierarchical labeling workflow and dashboards. In Part 1, we look at creating multi-step labeling workflows for hierarchical label taxonomies using AWS Step Functions. In Part 2 (coming soon), we look at how to build dashboards for analyzing dataset annotations and worker […]

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 […]

Annotate dense point cloud data using Amazon SageMaker Ground Truth

Autonomous vehicle companies typically use LiDAR sensors to generate a 3D understanding of the environment around their vehicles. For example, they mount a LiDAR sensor on their vehicles to continuously capture point-in-time snapshots of the surrounding 3D environment. The LiDAR sensor output is a sequence of 3D point cloud frames (the typical capture rate is […]

Monitor and Manage Anomaly Detection Models on a fleet of Wind Turbines with Amazon SageMaker Edge Manager

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. In industrial IoT, running machine learning (ML) models on edge devices is necessary for many use cases, such as predictive maintenance, quality improvement, real-time monitoring, process optimization, and security. The energy industry, for instance, invests heavily in ML to automate […]

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 […]

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 […]

Quality Assessment for SageMaker Ground Truth Video Object Tracking Annotations using Statistical Analysis

Data quality is an important topic for virtually all teams and systems deriving insights from data, especially teams and systems using machine learning (ML) models. Supervised ML is the task of learning a function that maps an input to an output based on examples of input-output pairs. For a supervised ML algorithm to effectively learn […]

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 […]

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 […]

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 […]