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Favorite In this post, we walk you through the steps to build machine learning (ML) models in Amazon SageMaker with data stored in Amazon HealthLake using two example predictive disease models we trained on sample data using the MIMIC-III dataset. This dataset was developed by the MIT lab for Computational
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Shared by AWS Machine Learning January 19, 2021
Favorite Prior to using any kind of supervised machine learning (ML) algorithm, data has to be labeled. Amazon SageMaker Ground Truth simplifies and accelerates this task. Ground Truth uses pre-defined templates to assign labels that classify the content of images or videos or verify existing labels. Ground Truth allows you
Read More Shared by AWS Machine Learning January 19, 2021
Favorite We recently announced Amazon SageMaker Pipelines, the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). SageMaker Pipelines is a native workflow orchestration tool for building ML pipelines that take advantage of direct Amazon SageMaker integration. Three components improve the operational resilience and reproducibility
Read More Shared by AWS Machine Learning January 19, 2021
Favorite Machine learning (ML)-based recommender systems aren’t a new concept across organizations such as retail, media and entertainment, and education, but developing such a system can be a resource-intensive task—from data labelling, training and inference, to scaling. You also need to apply continuous integration, continuous deployment, and continuous training to
Read More Shared by AWS Machine Learning January 14, 2021
Favorite Your knowledge store should support people who browse as well as people who search. It should be like a shopper-friendly supermarket. Image from wikimedia commons Some shoppers know exactly what they want. They walk into the relevant store, ask an assistant where to find the item, and buy it.
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Shared by Nick Milton January 14, 2021
Favorite A gym is a toolkit for developing and comparing reinforcement learning algorithms. Procgen Benchmark is a suite of 16 procedurally-generated gym environments designed to benchmark both sample efficiency and generalization in reinforcement learning. These environments are associated with the paper Leveraging Procedural Generation to Benchmark Reinforcement Learning (citation). Compared
Read More Shared by AWS Machine Learning January 13, 2021
Favorite Machine learning (ML) is used throughout the financial services industry to perform a wide variety of tasks, such as fraud detection, market surveillance, portfolio optimization, loan solvency prediction, direct marketing, and many others. This breadth of use cases has created a need for lines of business to quickly generate
Read More Shared by AWS Machine Learning January 13, 2021
Favorite According to the International Chamber of Shipping, 90% of world commerce happens at sea. Vessels are transporting every possible kind of commodity, including raw materials and semi-finished and finished goods, making ocean transportation a key component of the global supply chain. Manufacturers, retailers, and the end consumer are reliant
Read More Shared by AWS Machine Learning January 13, 2021
Favorite Amazon SageMaker Studio notebooks provide a full-featured integrated development environment (IDE) for flexible machine learning (ML) experimentation and development. Security measures secure and support a versatile and collaborative environment. In some cases, such as to protect sensitive data or meet regulatory requirements, security protocols require that public internet access
Read More Shared by AWS Machine Learning January 12, 2021
Favorite We’re excited to announce the global availability of AWS Contact Center Intelligence (AWS CCI) solutions powered by AWS AI Services and made available through the AWS Partner Network. AWS CCI solutions enable you to leverage AWS machine learning (ML) capabilities with your current contact center provider to gain greater
Read More Shared by AWS Machine Learning January 12, 2021