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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 lecturer and consultant at both UC Davis Graduate School of Management MSBA program and the Graduate Data Science program, MSDS, at Northwestern. He is teaching and designing graduate machine learning, AI, Data Science courses and consulting on Machine Learning and Cloud Architecture for students and faculty. These responsibilities including leading a multi-cloud certification initiative for students. He has published close to 100 technical publications including two books on subjects ranging from Cloud Machine Learning to DevOps. Gift received an MBA from UC Davis, a M.S. in Computer Information Systems from Cal State Los Angeles, and a B.S. in Nutritional Science from Cal Poly San Luis Obispo.

    Professionally, Noah has approximately 20 years’ experience programming in Python. He is a Python Software Foundation Fellow, AWS Subject Matter Expert (SME) on Machine Learning, AWS Certified Solutions Architect and AWS Academy Accredited Instructor, Google Certified Professional Cloud Architect, Microsoft MTA on Python. He has worked in roles ranging from CTO, General Manager, Consulting CTO and Cloud Architect. This experience has been with a wide variety of companies including ABC, Caltech, Sony Imageworks, Disney Feature Animation, Weta Digital, AT&T, Turner Studios and Linden Lab. In the last ten years, he has been responsible for shipping many new products at multiple companies that generated millions of dollars of revenue and had global scale. Currently he is consulting startups and other companies.


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)