AWS Machine Learning Blog

Category: Amazon SageMaker

How DeepMap optimizes their video inference workflow with Amazon SageMaker Processing

Although we might think the world is already sufficiently mapped by the advent of global satellite images and street views, it’s far from complete because much of the world is still uncharted territory. Maps are designed for humans, and can’t be consumed by autonomous vehicles, which need a very different technology of maps with much […]

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Detecting hidden but non-trivial problems in transfer learning models using Amazon SageMaker Debugger

Rapid development of deep learning technology has produced an abundance of open-sourced, pre-trained models in computer vision and natural language processing. As a result, transfer learning has become a popular approach in deep learning. Transfer learning is a machine learning technique where a model pre-trained on one task is fine-tuned on a new task. Given […]

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Predicting qualification ranking based on practice session performance for Formula 1 Grand Prix

If you’re a Formula 1 (F1) fan, have you ever wondered why F1 teams have very different performances between qualifying and practice sessions? Why do they have multiple practice sessions in the first place? Can practice session results actually tell something about the upcoming qualifying race? In this post, we answer these questions and more. […]

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Configuring Amazon SageMaker Studio for teams and groups with complete resource isolation

Amazon SageMaker is a fully managed service that provides every machine learning (ML) developer and data scientist with the ability to build, train, and deploy ML models quickly. Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for ML that lets you build, train, debug, deploy, and monitor your ML models. Amazon SageMaker Studio […]

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Bringing your own custom container image to Amazon SageMaker Studio notebooks

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). SageMaker Studio lets data scientists spin up Studio notebooks to explore data, build models, launch Amazon SageMaker training jobs, and deploy hosted endpoints. Studio notebooks come with a set of pre-built images, which consist of the Amazon SageMaker Python SDK […]

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Deploying reinforcement learning in production using Ray and Amazon SageMaker

Reinforcement learning (RL) is used to automate decision-making in a variety of domains, including games, autoscaling, finance, robotics, recommendations, and supply chain. Launched at AWS re:Invent 2018, Amazon SageMaker RL helps you quickly build, train, and deploy policies learned by RL. Ray is an open-source distributed execution framework that makes it easy to scale your […]

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Explaining Amazon SageMaker Autopilot models with SHAP

Machine learning (ML) models have long been considered black boxes because predictions from these models are hard to interpret. However, recently, several frameworks aiming at explaining ML models were proposed. Model interpretation can be divided into local and global explanations. A local explanation considers a single sample and answers questions like “Why does the model […]

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Real-time data labeling pipeline for ML workflows using Amazon SageMaker Ground Truth

High-quality machine learning (ML) models depend on accurately labeled, high-quality training, validation, and test data. As ML and deep learning models are increasingly integrated into production environments, it’s becoming more important than ever to have customizable, real-time data labeling pipelines that can continuously receive and process unlabeled data. For example, you may want to create […]

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Training and serving H2O models using Amazon SageMaker

Model training and serving steps are two essential pieces of a successful end-to-end machine learning (ML) pipeline. These two steps often require different software and hardware setups to provide the best mix for a production environment. Model training is optimized for a low-cost, feasible total run duration, scientific flexibility, and model interpretability objectives, whereas model […]

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This month in AWS Machine Learning: October edition

Every day there is something new going on in the world of AWS Machine Learning—from launches to new to use cases to interactive trainings. We’re packaging some of the not-to-miss information from the ML Blog and beyond for easy perusing each month. Check back at the end of each month for the latest roundup. Launches […]

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