Building, automating, managing, and scaling ML workflows using Amazon SageMaker Pipelines

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 of

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Shared by AWS Machine Learning January 19, 2021

Automating Amazon Personalize solution using the AWS Step Functions Data Science SDK

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 your

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Shared by AWS Machine Learning January 14, 2021

The concept of the Knowledge Supermarket, and how to apply it

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. Others

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Shared by Nick Milton January 14, 2021

How to train procedurally generated game-like environments at scale with Amazon SageMaker RL

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 to

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Shared by AWS Machine Learning January 13, 2021

Creating high-quality machine learning models for financial services using Amazon SageMaker Autopilot

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 high-quality

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Shared by AWS Machine Learning January 13, 2021

Hosting a private PyPI server for Amazon SageMaker Studio notebooks in a VPC

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 be

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Shared by AWS Machine Learning January 12, 2021