Tech workshop: “Spark MLlib: Past, Present, and Future”

Links:

• Registration link: http://tech-meetup-10-03-2015.eventbrite.com

• Event link: http://www.tech-meetup.com/events/10-03-2015

• Join tech-meetup community:

• LinkedIn group: https://www.linkedin.com/grp/members?gid=8362423

• Google group：http://www.tech-meetup.com/signup

• 微信群：http://www.tech-meetup.com/wechat

Time: 1:30PM ~ 4:00PM, 10/03/2015, Saturday

Location: 97 E Brokaw Rd, Ste 210, San Jose, CA 95112

Agenda:

1:30pm - 2:00pm: Reception and social time

2:00pm - 3:30pm: Talk and QA

3:30pm - 4:00pm: offline networking

Tech Talks Abstract:

Apache Spark provides primitives for in-memory cluster computing, which is well suited for large-scale machine learning purposes. MLlib is a standard component in Spark providing machine learning primitives, initially created and contributed to Spark by UC Berkeley. With 50+ companies and 180+ individual developers contributing to MLlib, it is one of the most active open source projects for machine learning. MLlib’s goal is to make practical machine learning scalable and easy, and the community has devoted lot of time and effort towards this goal. In this talk, we present a brief history of MLlib, summarize new features in Spark 1.5, and discuss the roadmap. We will show the expansion of MLlib’s feature set, the evolution of MLlib’s pipeline API, the elevation of MLlib’s performance, as well as the integration with other Spark components. We will also provide entry points for users and developers to get started with Spark MLlib.

Speaker’ bio:

Xiangrui Meng is an Apache Spark PMC member and a software engineer at Databricks. His main interests center around developing and implementing scalable algorithms for scientific applications. He has been actively involved in the development and maintenance of Spark MLlib since he joined Databricks. Before Databricks, he worked as an applied research engineer at LinkedIn, where he was the main developer of an offline machine learning framework in Hadoop MapReduce. His Ph.D. work at Stanford is on randomized algorithms for large-scale linear regression problems.

Ticket Info:

In the past events, many persons registered but didn’t present. It is hard for us to get the accurate participant number before each event, and it wasted a lot of opportunities for others who really wanted to attend. So, from this event, we introduce the refundable tickets: full refund if you present, but non-refundable if absent. The first 150 tickets are still “backward-compatible”: totally free.

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湾区同学技术沙龙（www.tech-meetup.com）

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