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Pragmatic AI: An Introduction To Cloud-based Machine Learning


Book Resources

This books was written in partnership with Pragmatic AI Labs.

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You can continue learning about these topics by:

Foundations of Data Engineering (Specialization: 4 Courses)
Publisher: Coursera + Duke
Release Date: 4/1/2022

Cloud Computing (Specialization: 4 Courses)

Publisher: Coursera + Duke

Release Date: 4/1/2021

Building Cloud Computing Solutions at Scale Specialization Launch Your Career in Cloud Computing. Master strategies and tools to become proficient in developing data science and machine learning (MLOps) solutions in the Cloud

What You Will Learn

  • Build websites involving serverless technology and virtual machines, using the best practices of DevOps
  • Apply Machine Learning Engineering to build a Flask web application that serves out Machine Learning predictions
  • Create Microservices using technologies like Flask and Kubernetes that are continuously deployed to a Cloud platform: AWS, Azure or GCP

Courses in Specialization


Pragmatic AI is the first truly practical guide to solving real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Writing for business professionals, decision-makers, and students who aren’t professional data scientists, Noah Gift demystifies all the tools and technologies you need to get results. He illuminates powerful off-the-shelf cloud-based solutions from Google, Amazon, and Microsoft, as well as accessible techniques using Python and R. Throughout, you’ll find simple, clear, and effective working solutions that show how to apply machine learning, AI and cloud computing together in virtually any organization, creating solutions that deliver results, and offer virtually unlimited scalability. Coverage includes:

  • Getting and configuring all the tools you’ll need
  • Quickly and efficiently deploying AI applications using spreadsheets, R, and Python
  • Mastering the full application lifecycle: Download, Extract, Transform, Model, Serve Results
  • Getting started with Cloud Machine Learning Services, Amazon’s AWS AI Services, and Microsoft’s Cognitive Services API
  • Uncovering signals in Facebook, Twitter and Wikipedia
  • Listening to channels via Slack bots running on AWS Lambda (serverless)
  • Retrieving data via the Twitter API and extract follower relationships
  • Solving project problems and find highly-productive developers for data science projects
  • Forecasting current and future home sales prices with Zillow
  • Using the increasingly popular Jupyter Notebook to create and share documents integrating live code, equations, visualizations, and text
  • And much more

Book Chapter Juypter Notebooks

Note, it is recommended to also watch companion Video Material: Essential Machine Learning and AI with Python and Jupyter Notebook


This code is released under the MIT license


The text content of notebooks is released under the CC-BY-NC-ND license

Additional Related Topics from Noah Gift

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