This document provides an introduction and overview of Hivemall, an open source machine learning library built as a collection of Hive UDFs. It begins with background on the presenter, Makoto Yui, and then covers the following key points:
- What Hivemall is and its vision of bringing machine learning capabilities to SQL users
- Popular algorithms supported in current and upcoming versions, such as random forest, factorization machines, gradient boosted trees
- Real-world use cases at companies such as for click-through rate prediction, user profiling, and churn detection
- How to use algorithms like random forest, matrix factorization, and factorization machines from SQL queries
- The development roadmap, with upcoming features including NLP
Podling Hivemall in the Apache IncubatorMakoto Yui
Hivemall is a scalable machine learning library built as a collection of Hive UDFs. It was accepted into the Apache Incubator in September 2016. Hivemall can be used across multiple platforms like Hive, Spark, Pig, and is designed to be easy to use, versatile, and scalable for large datasets. It allows SQL developers to perform machine learning tasks in a parallel and scalable way on Hadoop clusters.
This document provides an introduction and overview of Hivemall, an open source machine learning library built as a collection of Hive UDFs. It begins with background on the presenter, Makoto Yui, and then covers the following key points:
- What Hivemall is and its vision of bringing machine learning capabilities to SQL users
- Popular algorithms supported in current and upcoming versions, such as random forest, factorization machines, gradient boosted trees
- Real-world use cases at companies such as for click-through rate prediction, user profiling, and churn detection
- How to use algorithms like random forest, matrix factorization, and factorization machines from SQL queries
- The development roadmap, with upcoming features including NLP
Podling Hivemall in the Apache IncubatorMakoto Yui
Hivemall is a scalable machine learning library built as a collection of Hive UDFs. It was accepted into the Apache Incubator in September 2016. Hivemall can be used across multiple platforms like Hive, Spark, Pig, and is designed to be easy to use, versatile, and scalable for large datasets. It allows SQL developers to perform machine learning tasks in a parallel and scalable way on Hadoop clusters.
Interactive Designed and Curated Maps of Cities with StrolyToru Takahashi
Introduction the Stroly project. With the stroly service, the designed and curated maps of cities (maybe they are not accurate) are shared and available on smartphone with the users' GPS current positions. This presentation is talked in the International workshop of Kobe x Barcelona World Data Viz Challenge 2016.