Spark Summit 2017 kicks off in less than two weeks with a program that includes more than 175 talks led by top experts in the Apache Spark ecosystem. From developer tutorials and research demos to real-world case studies and data science applications, these 5 sessions will take your machine learning skills to the next level.
5 Machine Learning talks to check out at Spark Summit 2017:
Apache Spark MLlib’s Past Trajectory and New Directions\ (Joseph Bradley, Databricks) – This talk discusses the trajectory of MLlib, the Machine Learning (ML) library for Apache Spark. Review the history of the project, including major trends and efforts leading up to today.
Embracing a Taxonomy of Types to Simplify Machine Learning\ (Leah McGuire, Salesforce) – Salesforce has created a machine learning framework on top of Spark ML that builds personalized models for businesses across a range of applications.
Extending Spark Machine Learning: Adding Your Own Algorithms and Tools\ (Holden Karau & Seth Hendrickson, IBM) – Apache Spark’s machine learning (ML) pipelines provide a lot of power, but sometimes the tools you need for your specific problem aren’t available yet. This talk introduces Spark’s ML pipelines, and looks at how to extend them with your own custom algorithms.
How Apache Spark and AI Powers UberEATS\ (Chen Jin & Xian Xing Zhang, Uber) – The overall relevance and health of the UberEATS marketplace is critical in order to make and maintain it as an everyday product for Uber’s users. Chen and Xian Xian explain their implementation of Apache Spark and AI to increase user retention.
Real-Time Image Recognition with Apache Spark\ (Nikita Shamgunov, SingleStore) – Nikita’s session will examine the image recognition techniques available with Apache Spark, and how to put those techniques into production
Don’t miss the chance to see a live demo at the SingleStore kiosk! Visit us at kiosk #7 in the Moscone West expo hall or book a demo here.
Get The SingleStore Spark Connector Guide
The 79 page guide covers how to design, build, and deploy Spark applications using the SingleStore Spark Connector. Inside, you will find code samples to help you get started and performance recommendations for your production-ready Apache Spark and SingleStore implementations.\ Download Here