Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA
 
212 A-B
Add Apache Spark programming to your personal schedule
9:00am Apache Spark programming Brooke Wenig (Databricks)
212 C
Add Data science and machine learning with Apache Spark to your personal schedule
9:00am Data science and machine learning with Apache Spark Brian Bloechle (Cloudera, Inc.)
212 D
Add Data science for managers to your personal schedule
9:00am Data science for managers Angie Ma (ASI)
111
114
Add Real-time systems with Spark Streaming and Kafka to your personal schedule
9:00am Real-time systems with Spark Streaming and Kafka Jesse Anderson (Big Data Institute)
San Jose Ballroom (salon 1&2)
Add Machine learning with PyTorch to your personal schedule
9:00am Machine learning with PyTorch Delip Rao (R7 Speech Science), Brian McMahan (Joostware)
Willow Glen (1&2)
Add Hands-on data science with Python to your personal schedule
9:00am Hands-on data science with Python Robert Schroll (The Data Incubator)
9:00am-5:00pm (8h) Data science and machine learning
Apache Spark programming
Brooke Wenig (Databricks)
Brooke Wenig walks you through the core APIs for using Spark, fundamental mechanisms and basic internals of the framework, SQL and other high-level data access tools, and Spark’s streaming capabilities and machine learning APIs.
9:00am-5:00pm (8h)
Data science and machine learning with Apache Spark
Brian Bloechle (Cloudera, Inc.)
Brian Bloechle demonstrates how to implement typical data science workflows using Apache Spark. You'll learn how to wrangle and explore data using Spark SQL DataFrames and how to build, evaluate, and tune machine learning models using Spark MLlib.
9:00am-5:00pm (8h)
Data science for managers
Angie Ma (ASI)
Angie Ma offers a condensed introduction to key data science and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization.
9:00am-5:00pm (8h) Data science and machine learning
Machine learning with TensorFlow
The instructors demonstrate TensorFlow's capabilities through its Python interface and explore 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.
9:00am-5:00pm (8h) Big data and data science in the cloud
Real-time systems with Spark Streaming and Kafka
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)
Machine learning with PyTorch
Delip Rao (R7 Speech Science), Brian McMahan (Joostware)
PyTorch is a recent deep learning framework from Facebook that is gaining massive momentum in the deep learning community. Its fundamentally flexible design makes building and debugging models straightforward, simple, and fun. Delip Rao and Brian McMahan walk you through PyTorch's capabilities and demonstrate how to use PyTorch to build deep learning models and apply them to real-world problems.
9:00am-5:00pm (8h)
Hands-on data science with Python
Robert Schroll (The Data Incubator)
Robert Schroll offers an introduction to machine learning in Python, as he walks you through building an anomaly detection model and a recommendation engine. You'll gain hands-on experience from prototyping to production, and everything in between, including data cleaning, feature engineering, model building and evaluation, and deployment.