Practical Machine Learning on Images


In this workshop, you’ll build an end-to-end machine learning model for image understanding using Google Cloud Vertex AI. The workshop will show you how to prepare the 5-flowers dataset, train a Transfer Learning EfficientNet model to classify flowers, deploy the model, explain its predictions, and invoke the model from a streaming pipeline. The labs are based on the O’Reilly Media book “Practical Machine Learning for Computer Vision” by Valliappa Lakshmanan, Martin Gorner, and Ryan Gillard.

Session Outline
* Setup a Vertex AI Notebook instance
* Train a Transfer Learning model
* Prepare ML Datasets
* Train and export a SavedModel
* Deploy model to Vertex AI
* Create an ML Pipeline

Background Knowledge
Python, basics of ML


Lak is the Director for Data Analytics and AI Solutions on Google Cloud. His team builds software solutions for business problems using Google Cloud's data analytics and machine learning products. He founded Google's Advanced Solutions Lab ML Immersion program and is the author of three O'Reilly books and several Coursera courses. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA.

​Follow him on Twitter at @lak_gcp, read articles by him on Medium, and see more details at

Open Data Science




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