Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA

2-Day Training

All training courses take place 9:00am - 5:00pm, Monday, March 25 through Tuesday, March 26. In order to maintain a high level of hands-on learning and instructor interaction, each training course is limited in size.

Participants should plan to attend both days of this 2-day training course. To attend training courses, you must register for a Platinum or Training pass; does not include access to tutorials on Tuesday.

Monday, March 25 - Tuesday, March 26

Add to your personal schedule
9:00am - 5:00pm Monday, March 25 & Tuesday, March 26
Location: 2018
Secondary topics:  AI and Data technologies in the cloud, Storage
Jorge Lopez (Amazon Web Services)
Serverless technologies let you build and scale applications and services rapidly without the need to provision or manage servers. Jorge Lopez shows you how to incorporate serverless concepts into your big data architectures, looking at design patterns to ingest, store, and analyze your data. Then, you'll build a big data application using AWS technologies such as S3, Athena, Kinesis, and more Read more.
Add to your personal schedule
9:00am - 5:00pm Monday, March 25 & Tuesday, March 26
Location: 2014
Secondary topics:  Deep Learning
Robert Schroll (The Data Incubator)
The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. Robert Schroll offers an overview of TensorFlow's capabilities in Python, demonstrating how to build machine learning algorithms piece by piece and how to use TensorFlow's Keras API with several hands-on applications. Read more.
Add to your personal schedule
9:00am - 5:00pm Monday, March 25 & Tuesday, March 26
Location: 2016
Don Fox (The Data Incubator)
Don Fox walks you through developing a machine learning pipeline, from prototyping to production. You'll learn about data cleaning, feature engineering, model building and evaluation, and deployment and then extend these models into two applications from real-world datasets. All work will be done in Python. Read more.
Add to your personal schedule
9:00am - 5:00pm Monday, March 25 & Tuesday, March 26
Location: 2010
Secondary topics:  AI and machine learning in the enterprise
Michael Li (The Data Incubator), Rich Ott (The Data Incubator)
Michael Li and Rich Ott offer a nontechnical overview of AI and data science. Learn common techniques, how to apply them in your organization, and common pitfalls to avoid. You’ll pick up the language and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making. Read more.
Add to your personal schedule
9:00am - 5:00pm Monday, March 25 & Tuesday, March 26
Location: 3018
Secondary topics:  Deep Learning, Financial Services, Temporal data and time-series analytics
Francesca Lazzeri (Microsoft)
Francesca Lazzeri walks you through the core steps for using Azure Machine Learning services to train your machine learning models both locally and on remote compute resources. Read more.
Add to your personal schedule
9:00am - 5:00pm Monday, March 25 & Tuesday, March 26
Location: 3016
Secondary topics:  Streaming, realtime analytics, and IoT
Jesse Anderson (Big Data Institute)
Jesse Anderson leads a deep dive into Apache Kafka. You'll learn how Kafka works and how to create real-time systems with it. You'll also discover how to create consumers and publishers in Kafka and how to use Kafka Streams, Kafka Connect, and KSQL as you explore the Kafka ecosystem. Read more.
Add to your personal schedule
9:00am - 5:00pm Monday, March 25 & Tuesday, March 26
Location: 2020
Secondary topics:  Deep Learning
Ian Cook (Cloudera)
Advancing your career in data science requires learning new languages and frameworks—but learners face an overwhelming array of choices, each with different syntaxes, conventions, and terminology. Ian Cook simplifies the learning process by elucidating the abstractions common to these systems. Through hands-on exercises, you'll overcome obstacles to getting started using new tools. Read more.