Favorite Robotics often involves training complex sequences of behaviors. For example, consider a robot designed to follow or track another object. Although the goal is easy to describe (the closer the robot is to the object, the better), creating the logic that accomplishes the task is much more difficult. Reinforcement

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Shared by AWS Machine Learning May 30, 2019

Favorite During the past few years, the demand for machine learning specialists and engineers has soared. These two roles now rank among the top emerging jobs on LinkedIn. More recently, machine learning is being adopted by a wide range of industries, from medical diagnostic companies to finance firms and more.

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Shared by AWS Machine Learning May 30, 2019

Favorite The new release of MXNet 1.4 for Amazon Elastic Inference now includes Java and Scala support. Apache MXNet is an open source deep learning framework used to build, train, and deploy deep neural networks. Amazon Elastic Inference (EI) is a service that allows you to attach low-cost GPU-powered acceleration

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Shared by AWS Machine Learning May 30, 2019

Favorite Even in today’s highly digital workplace, documents are often manually processed in many enterprise workflows, including workflows in financial services.  Alkymi, founded by a team from Bloomberg and x.ai, enlists automation to streamline this laborious and error-prone work. Using deep learning models hosted on Amazon SageMaker, Alkymi identifies patterns

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Shared by AWS Machine Learning May 30, 2019

Favorite Today, we introduce four new features of Amazon SageMaker Object2Vec: negative sampling, sparse gradient update, weight-sharing, and comparator operator customization. Amazon SageMaker Object2Vec is a general-purpose neural embedding algorithm. If you’re unfamiliar with Object2Vec, see the blog post Introduction to Amazon SageMaker Object2Vec, which provides a high-level overview of the

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Shared by AWS Machine Learning May 30, 2019

Favorite Good machine learning models are built with large volumes of high-quality training data. But creating this kind of training data is expensive, complicated, and time-consuming. To help a model learn how to make the right decisions, you typically need a human to manually label the training data. Amazon SageMaker

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Shared by AWS Machine Learning May 30, 2019

Favorite It’s been a busy week for the AWS DeepRacer League. The world’s first global autonomous racing league allows machine learning developers of all skill levels to get hands-on with machine learning in a fun and exciting way. On April 29 2019, the virtual circuit of the AWS DeepRacer League

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Shared by AWS Machine Learning May 30, 2019

Favorite Machine learning (ML) workflows orchestrate and automate sequences of ML tasks by enabling data collection and transformation. This is followed by training, testing, and evaluating a ML model to achieve an outcome. For example, you might want to perform a query in Amazon Athena or aggregate and prepare data

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Shared by AWS Machine Learning May 30, 2019

Favorite The competition is heating up in the AWS DeepRacer League, the world’s first global autonomous racing league, open to anyone. The first round is almost halfway home, now that 9 of the 21 stops on the summit circuit schedule are complete. Developers continue to build new machine learning skills

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Shared by AWS Machine Learning May 30, 2019

Favorite Are you a data scientist with data warehouse tables that you’d like to explore in your machine learning (ML) environment? If so, read on. In this post, I show you how to perform exploratory analysis on large datasets stored in your data warehouse and cataloged in your AWS Glue

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Shared by AWS Machine Learning May 30, 2019