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

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

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Shared by AWS Machine Learning January 19, 2021

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

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

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Shared by AWS Machine Learning January 14, 2021

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

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

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

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

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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

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

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Shared by AWS Machine Learning January 12, 2021

AWS Announces the global expansion of AWS CCI Solutions

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

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Shared by AWS Machine Learning January 12, 2021