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Building AI Applications on Google Cloud Platform

Video Description

4+ Hours of Video Instruction


There is a rapid evolution occurring in machine learning with tools like AutoML that basically automate many of the tedious aspects of machine learning and allow developers to focus on getting results into production. Noah Gift illustrates just now to harness this technology and deploy it successfully on Google Cloud Platform, demonstrating for developers how to employ the current best practices and automated tools to create analytics applications that solve real-world problems.

Developers who want to take their Data Science skills to the next level and build AutoML applications in the Cloud will benefit from this unique course, as they learn how to use AutoML, Big Query, Python, and Google App Engine to create sophisticated AI.


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 LiveLesson 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.

Download the supplemental files for this LiveLesson from http://www.informit.com/store/building-ai-applications-on-google-cloud-platform-livelessons-9780135973509.

About the Instructor

Noah Gift is a lecturer at the UC Davis Graduate School of Management, MSBA program, the Graduate Data Science program, MSDS, at Northwestern and the Graduate Information and Data Science Program at UC Berkeley. He is teaching and designing graduate machine learning, AI, Data Science, and Cloud Architecture courses. These responsibilities include leading a multi-cloud certification initiative for students.

Noah is a Python Software Foundation Fellow, AWS Subject Matter Expert (SME) on Machine Learning, AWS Certified Solutions Architect, AWS Certified Big Data-Specialist, AWS Certified Machine Learning-Specialist, AWS Academy Accredited Instructor, Google Certified Professional Cloud Architect, Microsoft MTA on Python.

Noah was selected to the SME Machine Learning team due to accomplishments in the area of Machine Learning on the AWS platform. 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, an M.S. in Computer Information Systems from Cal State Los Angeles, and a B.S. in Nutritional Science from Cal Poly San Luis Obispo. Currently, he is consulting startups and other companies on Machine Learning, Cloud Architecture, and CTO-level consulting as the founder of Pragmatic AI Labs. His most recent book is Pragmatic AI: An Introduction to Cloud-Based Machine Learning (Pearson, 2018).

Skill Level

  • Beginning to Intermediate

What You Will Learn
Developers will learn how to:

  • Develop analytics applications using Python and GCP
  • Deploy analytics application on GCP
  • Learn to implement production AutoML
  • Solve real-world business problems

Who Should Take This Course
Primary: Data Scientists, Data Engineers, Software Engineers
Secondary: Graduate and Undergraduate students of Data Science, Data Engineering, and Computer Science

Course Requirements

  • Python:  6 months or greater.
  • Basic understanding of both linux and cloud computing

About Pearson Video Training

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.

Table of Contents

  1. Introduction
    1. Building AI Applications on Google Cloud Platform: Introduction 00:02:48
  2. Lesson 1: Create an Application Skeleton on GCP using Google App Engine
    1. Learning objectives 00:00:39
    2. 1.1 Setup gcloud environment 00:10:09
    3. 1.2 Create Hello World application 00:05:51
    4. 1.3 Deploy Hello World application 00:03:24
    5. 1.4 Modify and re-deploy Hello World application 00:04:23
    6. 1.5 Architect Analytics Applications overview 00:05:16
  3. Lesson 2: Build ETL (Extract Transform Load) Pipelines
    1. Learning objectives 00:00:27
    2. 2.1 ETL overview 00:03:20
    3. 2.2 Data flow demo 00:06:35
    4. 2.3 Cloud functions 00:06:01
    5. 2.4 GAE Cron jobs 00:02:57
  4. Lesson 3: Use ML Prediction on BigQuery
    1. Learning objectives 00:00:26
    2. 3.1 Introduction to Big Theory 00:05:13
    3. 3.2 Create Supervised ML predictions with BigQuery 00:08:44
    4. 3.3 Create K-Means Clustering with BigQuery 00:14:24
  5. Lesson 4: Use AutoML
    1. Learning objectives 00:00:24
    2. 4.1 AutoML overview 00:09:44
    3. 4.2 Use AutoML Vision 00:10:38
    4. 4.3 Use AutoML Tables 00:06:22
  6. Lesson 5: Use AI Platform
    1. Learning objectives 00:00:23
    2. 5.1 Explore AI APIs 00:03:10
    3. 5.2 Predict with AI Platform 00:06:40
    4. 5.3 Use Notebooks for Data Science explorations 00:08:15
  7. Lesson 6: Build an Analytics Application from Scratch
    1. Learning objectives 00:00:30
    2. 6.1 Create a new Analytics application from scratch 00:05:06
    3. 6.2 Deploy an Analytics application 00:08:26
    4. 6.3 Enhance an Analytics application 00:13:12
    5. 6.4 Enable functionality in an Analytics application 00:14:18
  8. Lesson 7: Using Build Systems and Containers
    1. Learning objectives 00:00:33
    2. 7.1 Architect continuous deployment systems 00:06:30
    3. 7.2 Configure your repository in Github 00:11:18
    4. 7.3 Configure makefile in Google Cloud Shell 00:04:15
    5. 7.4 Build your project in CircleCI 00:10:42
  9. Lesson 8: Course Overview
    1. Learning objectives 00:00:28
    2. 8.1 Course overview 00:04:42
  10. Summary
    1. Building AI Applications on Google Cloud Platform: Summary 00:01:56