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Building AI & ML Applications on Google Cloud Platform

Noah Gift

Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google’s state-of-the-art transfer learning and neural architecture search technology. Developers use Cloud AutoML’s graphical user interface to train, evaluate, improve, and deploy models based on their data.

This live training covers programming components essential to the development of AI and Analytics applications. The focus is on building real-world software engineering applications on the Google Cloud Platform. Several emerging technologies are used to demonstrate the process, including AutoML and Google BigQuery. The Python language is used throughout the course, as Python is becoming the de-facto standard language for AI application development in the cloud.

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

  • Learn to develop with GCP Cloud Shell
  • How to write GCP cloud functions in Python
  • Learn to implement cloud-native data engineering patterns, i.e. serverless
  • Learn to architect event-driven architectures on the GCP platform using: App Engine, AI APIs and AutoML

This training course is for you because...

  • You work with data and want to learn cloud-native data engineering patterns
  • You are new to the Google Cloud and want to learn to write functions in Python that do not require servers
  • Your a data scientist who needs a simpler way to get data engineering results
  • You want to learn about serverless technology and how to accomplish it in Python


  • Python: 6 months or greater
  • Basic understanding of both Linux and cloud computing
  • GCP free account
  • Chrome browser

Course Set-up:

  • GCP free account: https://console.cloud.google.com/
  • Web browser

Recommended Preparation:

Recommended Follow-up:

About your instructor

  • Noah Gift is a lecturer in the University of California, Berkeley, graduate data science program, the Northwestern University graduate data science program, and the MSBA program at the University of California, Davis, Graduate School of Management. He consults with startups and other companies on machine learning and cloud architecture and does CTO-level consulting as the founder of Pragmatic AI Labs. Noah has approximately 20 years’ experience programming in Python and is a Python Software Foundation Fellow. Previously, he worked for a variety of companies in roles such as CTO, general manager, consulting CTO, and cloud architect. He’s published over 100 technical publications, including books on cloud machine learning and DevOps, for O’Reilly, Pearson, DataCamp, Udacity, and other publishers. He’s also a certified AWS Solutions Architect. Noah earned an MBA from the University of California, Davis, an MS in computer information systems from California State University, Los Angeles, and a BS in nutritional science from Cal Poly, in San Luis Obispo. You can find more about Noah by following him on GitHub, visiting his website, or connecting with him on LinkedIn.


The timeframes are only estimates and may vary according to how the class is progressing

Part 1: Google App Engine (90 min)

  • Set up gcloud environment
  • Create a Hello World application
  • Deploy Hello World application
  • Modify and re-deploy Hello World application

Use Google BigQuery

  • Learn the basics of BQ
  • Learn to create predictions
  • Connect BigQuery and Google App Engine

Create ETL pipeline on GCP

  • Build deployment pipeline
  • ETL Pipelines with Cloud Functions and Scheduler
  • QA (15 min)
  • Break (15 min)

Part 2: Use ML Prediction on BigQuery (45 min)

Use BigQuery on public datasets

  • Create ML predictions with BigQuery
  • Connect ML predictions with Google App Engine

Connect Google Data Studio and BigQuery ML

  • Visualize Bike Data Clusters
  • QA (10 min)
  • Break (5 min)

Part 3: Use AI Platform & AutoML (45 min)

Explore AI APIs

  • Use the NLP API
  • Use the Vision API

Predict with AI Platform

  • Use Model deployment with AI Platform
  • Use Notebooks for Data Science explorations

Use AutoML

  • Use AutoML Vision
  • Use AutoML Tables
  • Connect AutoML to Google App Engine
  • QA (15 min)