October 28–31, 2019
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End-to-end machine learning with TensorFlow 2.0 on Google Cloud Platform

9:00am—5:00pm Monday, October 28—Tuesday, October 29
Location: Room 203

Participants should plan to attend both days of training course. Note: to attend training courses, you must be registered for a Platinum or Training pass; does not include access to tutorials on Tuesday.

Valliappa Lakshmanan shows you how to use Google Cloud Platform to design and build machine learning (ML) models and how to deploy them into production. You'll walk through the process of building a complete machine learning pipeline from ingest and exploration to training, evaluation, deployment, and prediction.

What you'll learn, and how you can apply it

  • Identify use cases for machine learning
  • Build an ML model using TensorFlow and build scalable, deployable ML models using Cloud ML
  • Learn how to incorporate advanced ML concepts into models and productionize trained ML models

Who is this presentation for?

  • You're a developer or data scientist.


  • A working knowledge of a common query language such as SQL and data modeling and extract, transform, load activities
  • Experience developing applications using a common programming language such as Python
  • Familiarity with statistics

Hardware and/or installation requirements:

  • A laptop
  • A Google account
  • A Google Cloud Platform account (Sign up for the free trial. You'll need a Google account and a credit card or bank account. Google services are currently unavailable in China. If you have a European Union (EU) or Russian billing address, please read the VAT overview documentation. More Google Cloud Platform free trial FAQs are available here.)


Day 1

  • Introduction to designing and building machine learning models on Google Cloud Platform
  • Learn machine learning, BigQuery, Keras, and TensorFlow 2.0 concepts
  • Hone skills in developing, evaluating, and productionizing ML models

Day 2

  • Build a complete ML pipeline covering ingest, exploration, training, evaluation, deployment, and prediction
  • Explore and split large datasets correctly using BigQuery
  • Develop the ML model in TensorFlow on a small sample locally, with the preprocessing operations implemented in Cloud Dataflow, so the same preprocessing can be applied in streaming mode
  • Distribute and scale the training model on Cloud AI Platform
  • Deploy the training model as a microservice
  • Invoke predictions from a web application
  • Automate the whole pipeline using Kubeflow Pipelines on Kubernetes

About your instructor

Photo of Valliappa Lakshmanan

Valliappa Lakshmanan is tech lead at Google Cloud focusing on data and machine learning. He’s the author of Data Science on GCP (O’Reilly), coauthor of BigQuery: The Definitive Guide (O’Reilly), and an instructor for multiple Coursera courses.

Conference registration

Get the Platinum pass or the Training pass to add this course to your package.

  • O'Reilly
  • TensorFlow
  • Google Cloud
  • IBM
  • Databricks
  • Tensor Networks
  • VMware
  • Amazon Web Services
  • One Convergence
  • Quantiphi
  • Lambda Labs
  • Tech Mahindra
  • cnvrg.io
  • Determined AI
  • Inferencery
  • Manceps, Inc.
  • PerceptiLabs
  • Valohai

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