Presented By O'Reilly and Cloudera
Make Data Work
March 13–14, 2017: Training
March 14–16, 2017: Tutorials & Conference
San Jose, CA
 
212 C
212 B
Add Data science at scale: Using Spark and Hadoop to your personal schedule
9:00am Data science at scale: Using Spark and Hadoop Bruce Martin (Cloudera)
213
Add Real-time data engineering in the cloud to your personal schedule
9:00am Real-time data engineering in the cloud Jesse Anderson (Big Data Institute)
212D
Add Machine learning with TensorFlow to your personal schedule
9:00am Machine learning with TensorFlow Robert Schroll (The Data Incubator)
7:30am Coffee break | Room: Executive Concourse
10:30am Morning break | Room: Executive Concourse
3:00pm Afternoon break | Room: Executive Concourse
12:30pm Break | Room: East Lobby
9:00am-5:00pm (8h) Spark & beyond Streaming
Spark foundations: Prototyping Spark use cases on Wikipedia datasets
Jacob D Parr (JParr Productions)
The real power and value proposition of Apache Spark is in building a unified use case that combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. Jacob Parr employs hands-on exercises using various Wikipedia datasets to illustrate the variety of ideal programming paradigms Spark makes possible.
9:00am-5:00pm (8h) Data science & advanced analytics, Spark & beyond
Data science at scale: Using Spark and Hadoop
Bruce Martin (Cloudera)
Bruce Martin walks you through applying data science methods to real-world challenges in different industries, offering preparation for data scientist roles in the field. Join in to learn how Spark and Hadoop enable data scientists to help companies reduce costs, increase profits, improve products, retain customers, and identify new opportunities.
9:00am-5:00pm (8h) Big data and the Cloud, Stream processing and analytics Architecture, Cloud
Real-time data engineering in the cloud
Jesse Anderson (Big Data Institute)
To handle real-time big data, you need to solve two difficult problems: how do you ingest that much data and how will you process that much data? Jesse Anderson explores the latest real-time frameworks (both open source and managed cloud services), discusses the leading cloud providers, and explains how to choose the right one for your company.
9:00am-5:00pm (8h) Data science & advanced analytics Deep learning
Machine learning with TensorFlow
Robert Schroll (The Data Incubator)
Robert Schroll demonstrates TensorFlow's capabilities through its Python interface and explores TFLearn, a high-level deep learning library built on TensorFlow. Join in to learn how to use TFLearn and TensorFlow to build machine-learning models on real-world data.
7:30am-9:00am (1h 30m)
Break: Coffee break
10:30am-11:00am (30m)
Break: Morning break
3:00pm-3:30pm (30m)
Break: Afternoon break
12:30pm-1:30pm (1h)
Break