Introducing Amazon SageMaker Reinforcement Learning Components for open-source Kubeflow pipelines

Favorite This blog post was co-authored by AWS and Max Kelsen. Max Kelsen is one of Australia’s leading Artificial Intelligence (AI) and Machine Learning (ML) solutions businesses. The company delivers innovation, directly linked to the generation of business value and competitive advantage to customers in Australia and globally, including Fortune

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Shared by AWS Machine Learning March 4, 2021

Utilizing XGBoost training reports to improve your models

Favorite In 2019, AWS unveiled Amazon SageMaker Debugger, a SageMaker capability that enables you to automatically detect a variety of issues that may arise while a model is being trained. SageMaker Debugger captures model state data at specified intervals during a training job. With this data, SageMaker Debugger can detect

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Shared by AWS Machine Learning March 4, 2021

Translate and analyze text using SQL functions with Amazon Athena, Amazon Translate, and Amazon Comprehend

Favorite You have Amazon Simple Storage Service (Amazon S3) buckets full of files containing incoming customer chats, product reviews, and social media feeds, in many languages. Your task is to identify the products that people are talking about, determine if they’re expressing happy thoughts or sad thoughts, translate their comments

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Shared by AWS Machine Learning February 27, 2021

The importance of Joined-up KM

Favorite One of the major differences between a Knowledge Management Toolbox and a Knowledge Management Framework is that in a framework, the components are joined up. Image from wikimedia commons I have blogged before about the evolution in KM thinking from tool, to toolkit, to framework. I have argued that

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Shared by Nick Milton February 26, 2021