Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK
 
Capital Suite 7
Capital Suite 17
Add Large-scale ML with MLflow, deep learning, and Apache Spark to your personal schedule
9:00 Training Large-scale ML with MLflow, deep learning, and Apache Spark Amir Issaei (Databricks)
Capital Suite 9
Add Machine learning from scratch in TensorFlow to your personal schedule
9:00 Training Machine learning from scratch in TensorFlow Ana Hocevar (The Data Incubator)
Capital Suite 1
Add Hands-on data science with Python to your personal schedule
9:00 Training Hands-on data science with Python Robert Schroll (The Data Incubator)
London Suite 2
Add Professional Kafka development to your personal schedule
9:00 Training Professional Kafka development Jesse Anderson (Big Data Institute)
Capital Suite 16
Add AI for managers to your personal schedule
9:00 Training AI for managers Nijma Khan (Faculty ai), Alberto Favaro (Faculty)
London Suite 3
Add Building a serverless big data application on AWS to your personal schedule
9:00 Training Building a serverless big data application on AWS Jorge Lopez (Amazon Web Services), Nikki Rouda (Amazon Web Services), Damon Cortesi (Amazon Web Services), Sven Hansen (Amazon Web Services), Manos Samatas (Amazon Web Services), Alket Memushaj (Amazon Web Services)
7:30 Early morning break | Room: Capital Suite Foyer
10:30 Morning 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) Data Science, Machine Learning & AI Deep Learning
Expand your data science and machine learning skills with Python, R, SQL, Spark, and TensorFlow
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.
9:00-17:00 (8h) Data Science, Machine Learning & AI Deep Learning, Model lifecycle management
Large-scale ML with MLflow, deep learning, and Apache Spark
Amir Issaei (Databricks)
Join Amir Issaei to explore neural network fundamentals and learn how to build distributed Keras/TensorFlow models on top of Spark DataFrames. You'll use Keras, TensorFlow, Deep Learning Pipelines, and Horovod to build and tune models and MLflow to track experiments and manage the machine learning lifecycle. This course is taught entirely in Python.
9:00-17:00 (8h) Data Science, Machine Learning & AI Deep Learning
Machine learning from scratch in TensorFlow
Ana Hocevar (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. Ana Hocevar offers an intro to TensorFlow's capabilities in Python, taking you from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications.
9:00-17:00 (8h) Data Science, Machine Learning & AI Data preparation, data governance, and data lineage
Hands-on data science with Python
Robert Schroll (The Data Incubator)
Robert Schroll walks 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 then extend these models into two applications from real-world datasets. All work will be done in Python.
9:00-17:00 (8h) Data Engineering and Architecture Data Integration and Data Pipelines, Streaming and realtime analytics
Professional Kafka development
Jesse Anderson (Big Data Institute)
Jesse Anderson offers an in-depth look at Apache Kafka. You'll learn how Kafka works and how to create real-time systems with it as well as how to create consumers and publishers. Jesse then walks you through Kafka’s ecosystem, demonstrating how to use tools like Kafka Streams, Kafka Connect, and KSQL.
9:00-17:00 (8h) Strata Business Summit AI and machine learning in the enterprise
AI for managers
Nijma Khan (Faculty ai), Alberto Favaro (Faculty)
Nijma Khan and Alberto Favaro offer a condensed introduction to key AI 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.
9:00-17:00 (8h) Data Engineering and Architecture AI and Data technologies in the cloud, Data Integration and Data Pipelines
Building a serverless big data application on AWS
Jorge Lopez (Amazon Web Services), Nikki Rouda (Amazon Web Services), Damon Cortesi (Amazon Web Services), Sven Hansen (Amazon Web Services), Manos Samatas (Amazon Web Services), Alket Memushaj (Amazon Web Services)
Serverless technologies let you build and scale applications and services rapidly without the need to provision or manage servers. Join in to learn how to incorporate serverless concepts into your big data architectures. You'll explore design patterns to ingest, store, and analyze your data as you build a big data application using AWS technologies such as S3, Athena, Kinesis, and more.
7:30-9:00 (1h 30m)
Break: Early morning break
10:30-11:00 (30m)
Break: Morning break
12:30-13:30 (1h)
Break: Lunch
15:00-15:30 (30m)
Break: Afternoon break