Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK
 
Capital Suite 7
Add Hands-on data science with Python to your personal schedule
9:00 Hands-on data science with Python Zachary Glassman (The Data Incubator)
Capital Suite 17
Add Machine learning with TensorFlow to your personal schedule
9:00 Machine learning with TensorFlow Dana Mastropole (The Data Incubator)
Capital Suite 16
Add Real-time systems with Spark Streaming and Kafka to your personal schedule
9:00 Real-time systems with Spark Streaming and Kafka Jesse Anderson (Big Data Institute)
Capital Suite 1
London Suite 2
Add Data science for managers to your personal schedule
9:00 Data science for managers Jean Innes (ASI Data Science), Matthew Ward (ASI Data Science)
7:30 Coffee break | Room: Capital Suite Foyer
10:30 Coffee break | Room: Capital Suite Foyer
12:30 Lunch | Room: Capital Suite Foyer
15:00 Afternoon break | Room: Capital Suite Foyer
9:00-17:00 (8h)
Hands-on data science with Python
Zachary Glassman (The Data Incubator)
Zachary Glassman offers a foundation in building intelligent business applications using machine learning, walking you through all the steps of developing a machine learning pipeline, from prototyping to production. You'll explore data cleaning, feature engineering, model building and evaluation, and deployment and extend these models into two applications using real-world datasets.
9:00-17:00 (8h)
Machine learning with TensorFlow
Dana Mastropole (The Data Incubator)
The TensorFlow library enables the use of data flow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs. This architecture makes it ideal for implementing neural networks and other machine learning algorithms. Dana Mastropole details TensorFlow's capabilities through its Python interface.
9:00-17:00 (8h) Data engineering and architecture, Streaming systems and real-time applications
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.
9:00-17:00 (8h)
Data science and machine learning with Apache Spark (SOLD OUT)
Behzad Bordbar (Cloudera)
Behzad Bordbar 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.
9:00-17:00 (8h)
Data science for managers
Jean Innes (ASI Data Science), Matthew Ward (ASI Data Science)
Jean Innes, Matthew Ward, Emanuele Haerens, and Alli Paget lead 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.
7:30-9:00 (1h 30m)
Break: Coffee break
10:30-11:00 (30m)
Break: Coffee break
12:30-13:30 (1h)
Break: Lunch
15:00-15:30 (30m)
Break: Afternoon break