Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Cross-Cloud Model Training and Serving with Kubeflow

Holden Karau (Google), Trevor Grant (IBM), Ilan Filonenko (Bloomberg LP), Francesca Lazzeri (Microsoft)
9:0012:30 Tuesday, 30 April 2019
Data Science, Machine Learning & AI
Location: Capital Suite 2/3
Secondary topics:  AI and Data technologies in the cloud, Model lifecycle management

Who is this presentation for?

ML Engineers and Data Scientist looking to simplify there model lifecycle



Prerequisite knowledge

Basic understanding of ML

Materials or downloads needed in advance

External SSH access

What you'll learn

Understanding of how to train and serve models regardless of cloud provider with Kubeflow


This workshop will quickly introduce what Kubeflow is, and how we can use it to train and serve models. The morning season will involve training a model on either Google Kubernetes Engine or on the students own laptop using minikube (as desired); and in the afternoon we will take the model trained and deploy it to the students choice of Google, Amazon, Microsoft, or IBM’s cloud.

To keep it simple we’ll focus on training on a simple model first, and for those who speed through everything you can either keep deploying more more clouds (gotta catch ‘em all), or try training a more complex/realistic model doing feature pre-processing.

Note: Student accounts will be provided for Google cloud, but users of other clouds will have to use their own accounts.

Photo of Holden Karau

Holden Karau


Holden Karau is a transgender Canadian open source developer advocate at Google focusing on Apache Spark, Beam, and related big data tools. Previously, she worked at IBM, Alpine, Databricks, Google (yes, this is her second time), Foursquare, and Amazon. Holden is the coauthor of Learning Spark, High Performance Spark, and another Spark book that’s a bit more out of date. She is a committer on the Apache Spark, SystemML, and Mahout projects. When not in San Francisco, Holden speaks internationally about different big data technologies (mostly Spark). She was tricked into the world of big data while trying to improve search and recommendation systems and has long since forgotten her original goal. Outside of work, she enjoys playing with fire, riding scooters, and dancing.

Photo of Trevor Grant

Trevor Grant


Trevor Grant is committer on the Apache Mahout, and contributor on Apache Streams (incubating), Apache Zeppelin, and Apache Flink projects and Open Source Technical Evangelist at IBM. In former rolls he called himself a data scientist, but the term is so over used these days. He holds an MS in Applied Math and an MBA from Illinois State University. Trevor is an organizer of the newly formed Chicago Apache Flink Meet Up, and has presented at Flink Forward, ApacheCon, Apache Big Data, and other meetups nationwide.

Trevor was a combat medic in Afghanistan in 2009, and wrote an award winning undergraduate thesis between missions. He has a dog and a cat and a 64 Ford and he loves them all very much.

Photo of Ilan  Filonenko

Ilan Filonenko

Bloomberg LP

Ilan Filonenko is a member of the Data Science Infrastructure team at Bloomberg, where he has designed and implemented distributed systems at both the application and infrastructure level. He is one of the principle contributors to Spark on Kubernetes, primarily focusing on the effort to enabled Secure HDFS interaction and non-JVM support. Previously, Ilan was an engineering consultant and technical lead in various startups and research divisions across multiple industry verticals, including medicine, hospitality, finance, and music. Ilan’s currently researches algorithmic, software, and hardware techniques for high-performance machine learning, with a focus on optimizing stochastic algorithms and model management.

Photo of Francesca Lazzeri

Francesca Lazzeri


Francesca Lazzeri is an AI and machine learning scientist on the cloud developer advocacy team at Microsoft. Francesca has multiple years of experience as data scientist and data-driven business strategy expert; she is passionate about innovations in big data technologies and the applications of machine learning-based solutions to real-world problems. Her work on these issues covers a wide range of industries, including energy, oil and gas, retail, aerospace, healthcare, and professional services. Previously, she was a research fellow in business economics at Harvard Business School, where she performed statistical and econometric analysis within the Technology and Operations Management Unit and worked on multiple patent data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation. Francesca is a mentor for PhD and postdoc students at the Massachusetts Institute of Technology and enjoys speaking at academic and industry conferences to share her knowledge and passion for AI, machine learning, and coding. Francesca holds a PhD in innovation management.

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