Favorite Amazon SageMaker Pipelines is the first purpose-built CI/CD service for machine learning (ML). It helps you build, automate, manage, and scale end-to-end ML workflows and apply DevOps best practices of CI/CD to ML (also known as MLOps). Creating multiple accounts to organize all the resources of your organization is

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Shared by AWS Machine Learning January 22, 2021

Favorite Amazon Comprehend is a natural language processing (NLP) service that uses machine learning (ML) to find insights and relationships in text. The service can extract people, places, sentiments, and topics in unstructured data. You can now use Amazon Comprehend ML capabilities to detect and redact personally identifiable information (PII)

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Shared by AWS Machine Learning January 20, 2021

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

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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

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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

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Shared by AWS Machine Learning 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

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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

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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

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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

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Shared by AWS Machine Learning January 12, 2021