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TransmogrifAI - TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Spark with minimal hand tuning

  •    Scala

TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library written in Scala that runs on top of Spark. It was developed with a focus on accelerating machine learning developer productivity through machine learning automation, and an API that enforces compile-time type-safety, modularity, and reuse. Through automation, it achieves accuracies close to hand-tuned models with almost 100x reduction in time. Skip to Quick Start and Documentation.

LearnAnalytics-mr4ds - R and Microsoft R Workflows for Data Science

  •    HTML

Welcome to the Microsoft R for Data Science Course Repository. You can find the latest materials from the workshop here, and links for course materials from prior iterations of the course ca be found in the version pane. While this course is intended for data scientists and analysts interested in the Microsoft R programming stack (i.e., Microsoft employees in the Algorithms and Data Science group), other programmers might find the material useful as well.Please refer to the course syllabus for the full syllabus. The goal of this course is to cover the following modules, although some of the latter modules may be repalced for a hackathon/office hours.

sagemaker-sparkml-serving-container - This code is used to build & run a Docker container for performing predictions against a Spark ML Pipeline

  •    Java

SageMaker SparkML Serving Container lets you deploy an Apache Spark ML Pipeline in Amazon SageMaker for real-time, batch prediction and inference pipeline use-cases. The container can be used to deploy a Spark ML Pipeline outside of SageMaker as well. It is powered by open-source MLeap library. Apache Spark is a unified analytics engine for large scale data processing. Apache Spark comes with a Machine Learning library called MLlib which lets you build ML pipelines using most of the standard feature transformers & algorithms. Apache Spark is well suited for batch processing use-cases and is not the preferred solution for low latency online inference scenarios. In order to perform low latency online prediction, SageMaker SparkML Serving Container leverages an open source library called MLeap.

SparkML - spark 机器学习:利用jupyter工作来讲解算法原理并运行相关例子

  •    Jupyter

spark 机器学习:利用jupyter工作来讲解算法原理并运行相关例子