Real-time data labeling pipeline for ML workflows using Amazon SageMaker Ground Truth

Favorite High-quality machine learning (ML) models depend on accurately labeled, high-quality training, validation, and test data. As ML and deep learning models are increasingly integrated into production environments, it’s becoming more important than ever to have customizable, real-time data labeling pipelines that can continuously receive and process unlabeled data. For

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Shared by AWS Machine Learning November 3, 2020

When "easier to share" means "harder to learn"

Favorite Easier to share can mean harder to learn Image from wikimedia commons A common mistake companies often make when it comes to setting up knowledge sharing systems is to make it as easy as possible for people to share. I know that doesn’t sound like a mistake, but let

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Shared by Nick Milton November 2, 2020

Halodoc uses AI to improve how doctors receive feedback

Favorite Due to Indonesia’s vast size and population, timely and reliable access to healthcare can sometimes be a challenge. Halodoc aims to change that with a mobile first-telemedicine platform that connects Indonesians to doctors and helps them arrange appointments, medicine deliveries and tests.  What’s distinctive about the Halodoc platform is

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Shared by Google AI Technology November 2, 2020

Training and serving H2O models using Amazon SageMaker

Favorite Model training and serving steps are two essential pieces of a successful end-to-end machine learning (ML) pipeline. These two steps often require different software and hardware setups to provide the best mix for a production environment. Model training is optimized for a low-cost, feasible total run duration, scientific flexibility,

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Shared by AWS Machine Learning October 31, 2020

Building a real-time conversational analytics platform for Amazon Lex bots

Favorite Conversational interfaces like chatbots have become an important channel for brands to communicate with their customers, partners, and employees. They offer faster service, 24/7 availability, and lower service costs. By analyzing your bot’s customer conversations, you can discover challenges in user experience, trending topics, and missed utterances. These additional

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Shared by AWS Machine Learning October 30, 2020

You never stop learning, but you should start teaching

Favorite In a learning organisation we are all learners, but over time each individual moves towards being a teacher as well When an employee is very new to an organisation or to a topic, they are usually quite quiet in KM activities; in lesson capture meetings for example, or on

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Shared by Nick Milton October 29, 2020