AWS Machine Learning Blog

Category: Amazon SageMaker

RAPIDS and Amazon SageMaker: Scale up and scale out to tackle ML challenges

In this post, we combine the powers of NVIDIA RAPIDS and Amazon SageMaker to accelerate hyperparameter optimization (HPO). HPO runs many training jobs on your dataset using different settings to find the best-performing model configuration. HPO helps data scientists reach top performance, and is applied when models go into production, or to periodically refresh deployed […]

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The following images show an example (left) where the model predicted every helmet correctly

Helmet detection error analysis in football videos using Amazon SageMaker

The National Football League (NFL) is America’s most popular sports league. Founded in 1920, the NFL developed the model for the successful modern sports league and is committed to advancing progress in the diagnosis, prevention, and treatment of sports-related injuries. Health and safety efforts include support for independent medical research and engineering advancements in addition […]

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Explaining Bundesliga Match Facts xGoals using Amazon SageMaker Clarify

One of the most exciting AWS re:Invent 2020 announcements was a new Amazon SageMaker feature, purpose built to help detect bias in machine learning (ML) models and explain model predictions: Amazon SageMaker Clarify. In today’s world where predictions are made by ML algorithms at scale, it’s increasingly important for large tech organizations to be able […]

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Perform interactive data processing using Spark in Amazon SageMaker Studio Notebooks

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). With a single click, data scientists and developers can quickly spin up Studio notebooks to explore datasets and build models. You can now use Studio notebooks to securely connect to Amazon EMR clusters and prepare vast amounts of data for […]

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How Latent Space used the Amazon SageMaker model parallelism library to push the frontiers of large-scale transformers

This blog is co-authored by Sarah Jane Hong CSO, Darryl Barnhart CTO, and Ian Thompson CEO of Latent Space and Prem Ranga of AWS. Latent space is a hidden representation of abstract ideas that machine learning (ML) models learn. For example, “dog,” “flower,” or “door” are concepts or locations in latent space. At Latent Space, […]

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The following diagram illustrates the architecture of the data processing and pipeline.

Multimodal deep learning approach for event detection in sports using Amazon SageMaker

Have you ever thought about how artificial intelligence could be used to detect events during live sports broadcasts? With machine learning (ML) techniques, we introduce a scalable multimodal solution for event detection on sports video data. Recent developments in deep learning show that event detection algorithms are performing well on sports data [1]; however, they’re […]

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From the following confusion matrix, we can see that the model does a better job at predicting for class 0 than class 1.

Utilizing XGBoost training reports to improve your models

In 2019, AWS unveiled Amazon SageMaker Debugger, a SageMaker capability that enables you to automatically detect a variety of issues that may arise while a model is being trained. SageMaker Debugger captures model state data at specified intervals during a training job. With this data, SageMaker Debugger can detect training issues or anomalies by leveraging […]

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Ripley is a Clearpath Robotics Husky equipped with two Universal Robotics UR5 arms.

Introducing Amazon SageMaker Reinforcement Learning Components for open-source Kubeflow pipelines

This blog post was co-authored by AWS and Max Kelsen. Max Kelsen is one of Australia’s leading Artificial Intelligence (AI) and Machine Learning (ML) solutions businesses. The company delivers innovation, directly linked to the generation of business value and competitive advantage to customers in Australia and globally, including Fortune 500 companies. Max Kelsen is also […]

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Analyzing open-source ML pipeline models in real time using Amazon SageMaker Debugger

Open-source workflow managers are popular because they make it easy to orchestrate machine learning (ML) jobs for productions. Taking models into productions following a GitOps pattern is best managed by a container-friendly workflow manager, also known as MLOps. Kubeflow Pipelines (KFP) is one of the Kubernetes-based workflow managers used today. However, it doesn’t provide all […]

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The following diagram shows our end-to-end automated MLOps pipeline

Architect and build the full machine learning lifecycle with AWS: An end-to-end Amazon SageMaker demo

In this tutorial, we will walk through the entire machine learning (ML) lifecycle and show you how to architect and build an ML use case end to end using Amazon SageMaker. Amazon SageMaker provides a rich set of capabilities that enable data scientists, machine learning engineers, and developers to prepare, build, train, and deploy ML […]

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