AWS Machine Learning Blog

Category: Amazon SageMaker

Support for Apache MXNet 1.4 and Model Server in Amazon SageMaker

Apache MXNet is an open-source deep learning software framework used to train and deploy deep neural networks. Data scientists and machine learning (ML) developers love MXNet due to its flexibility and efficiency when building deep learning models. Amazon SageMaker is committed to improving the customer experience for all ML frameworks and libraries, including MXNet. With the latest release of […]

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Train and deploy Keras models with TensorFlow and Apache MXNet on Amazon SageMaker

Keras is a popular and well-documented open source library for deep learning, while Amazon SageMaker provides you with easy tools to train and optimize machine learning models. Until now, you had to build a custom container to use both, but Keras is now part of the built-in TensorFlow environments for TensorFlow and Apache MXNet. Not […]

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Turning unstructured text into insights with Bewgle powered by AWS

Bewgle is an SAP.iO, Techstars-funded company that uses AWS services to surface insights from user-generated text and audio streams. Bewgle generates insights to help product managers to increase customer satisfaction and engagement with their various products—beauty, electronics, or anything in between.  By listening to the voices of their customers with the help of Bewgle powered […]

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Powering a search engine with Amazon SageMaker

This is a guest post by Evan Harris, Manager of Machine Learning at Ibotta. In their own words, “Ibotta is transforming the shopping experience by making it easy for consumers to earn cash back on everyday purchases through a single smartphone app. The company partners with leading brands and retailers to offer offers on groceries, […]

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Exploring data warehouse tables with machine learning and Amazon SageMaker notebooks

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 Data Catalog from your Amazon SageMaker […]

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Build end-to-end machine learning workflows with Amazon SageMaker and Apache Airflow

Updated 10/4/2019 to fix dependency and version issues with Amazon SageMaker and fixed delimiter issues when preparing scripts. 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 […]

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Amazon SageMaker Object2Vec adds new features that support automatic negative sampling and speed up training

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 algorithm with links to four notebook […]

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End document drudgery with Alkymi’s AWS-powered automated data entry and document insights

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 and relationships in unstructured data and […]

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Udacity’s Machine Learning Nanodegree now includes Amazon SageMaker 

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. Udacity created the Intro to Machine […]

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Analyze content with Amazon Comprehend and Amazon SageMaker notebooks

In today’s connected world, it’s important for companies to monitor social media channels to protect their brand and customer relationships. Companies try to learn about their customers, products, and services through social media, emails, and other communications. Machine learning (ML) models can help address some of these needs. However, the process to build and train […]

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