AWS Machine Learning Blog

Tag: Amazon EMR

Build Amazon SageMaker notebooks backed by Spark in Amazon EMR

Introduced at AWS re:Invent in 2017, Amazon SageMaker provides a fully managed service for data science and machine learning workflows. One of the important parts of Amazon SageMaker is the powerful Jupyter notebook interface, which can be used to build models. You can enhance the Amazon SageMaker capabilities by connecting the notebook instance to an […]

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Distributed Inference Using Apache MXNet and Apache Spark on Amazon EMR

In this blog post we demonstrate how to run distributed offline inference on large datasets using Apache MXNet (incubating) and Apache Spark on Amazon EMR. We explain how offline inference is useful, why it is challenging, and how you can leverage MXNet and Spark on Amazon EMR to overcome these challenges. Distributed inference on large […]

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Run Deep Learning Frameworks with GPU Instance Types on Amazon EMR

Today, AWS is excited to announce support for Apache MXNet and new generation GPU instance types on Amazon EMR, which enables you to run distributed deep neural networks alongside your machine learning workflows and big data processing. Additionally, you can install and run custom deep learning libraries on your EMR clusters with GPU hardware. Through […]

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Build PMML-based Applications and Generate Predictions in AWS

If you generate machine learning (ML) models, you know that the key challenge is exporting and importing them into other frameworks to separate model generation and prediction. Many applications use PMML (Predictive Model Markup Language) to move ML models from one framework to another. PMML is an XML representation of a data mining model. In […]

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