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Talk 1: Using Spark MLlib To Predict Most Popular Tweets
Spark's Machine Learning Library (MLlib) enables running Machine Learning
algorithms in a scalable way on massive datasets. In this talk we will use
Spark and MLlib to analyze tweets and predict the number of stars and retweets
that a tweet will get. The talk will include a tutorial on Spark and MLlib.
Prerequisites:
Beginner. Familiarity with a programming language will be helpful.
What You'll Learn:
After this talk you will be able to:
1. Use Spark to process large data sets.
2. Use Spark MLlib to apply Machine Learning algorithms to large data sets.
3. Understand pros and cons of using Spark vs other Machine Learning
technologies.
What to Bring:
Asim will share source code for the talk. Attendees can bring a laptop to
download and try the demos on their own machines.
Meet Your Speaker:
Asim Jalis is a Lead Instructor in Data Engineering at Galvanize. He has worked
as a software engineer and instructor at Cloudera, Microsoft, and
Hewlett-Packard. He has an MS in computer science from the University of
Virginia and an MA in mathematics from the University of Wisconsin—Madison.
Talk 2: Using Zeppelin Notebooks for Spark Streaming and Live Monitoring
We will discuss the rapidly evolving open source Zeppelin notebook project and
how it can be used for data science applications, including those with
streaming data. Zeppelin notebooks can use the scheduler functionality to
update data and generate plots. This allows for live monitoring applications to
be rapidly prototyped in a production environment. Simple end to end examples
will be discussed.
Prerequisites:
Intermediate to Advanced. Some experience with Spark and Zeppelin (or notebooks
in general) will be helpful.
Meet Your Speaker:
Jerome Nilmeier is a Data Scientist and Engineer at IBM in the Spark Enablement
Team and the Spark Technology Center, where he works on all things Spark
related. He contributes to open source, participates in community outreach, and
works with clients on Spark in production environments.
Prior to his journey into big data, he was a computational scientist at the
Lawrence Livermore and Berkeley National Laboratories. He holds a PhD in
Computational Biophysics from UC San Francisco, and a BS from UC Berkeley in
Chemical Engineering.
*Note, expedite your check in at Galvanize and register here
