Pragmatic AI: An Introduction To Cloud-based Machine Learning
Book Resources
This books was written in partnership with Pragmatic AI Labs.
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
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Taking the course AWS Certified Cloud Practitioner 2020-Real World & Pragmatic.
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Buying a copy of Pragmatic AI: An Introduction to Cloud-Based Machine Learning
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Reading book online on Safari: Online Version of Pragmatic AI: An Introduction to Cloud-Based Machine Learning, First Edition
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Watching 8+ Hour Video Series on Safari: Essential Machine Learning and AI with Python and Jupyter Notebook
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Viewing more content at noahgift.com
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Viewing more content at Pragmatic AI Labs
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Exploring related colab notebooks from Safari Online Training
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Learning about emerging topics in Hardware AI & Managed/AutoML
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Viewing more content on the Pragmatic AI Labs YouTube Channel
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Reading content on Pragmatic AI Medium
About
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
- Chapter 1: Introduction to Pragmatic AI
- Chapter 2: AI & ML Toolchain
- Chapter 3: Spartan AI Lifecyle
- Chapter 4: Cloud AI Development with Google Cloud Platform
- Chapter 5: Cloud AI Development with Amazon Web Services
- Chapter 6: Social Power NBA
- Chapter 7: Creating an Intelligent Slack Bot on AWS
- Chapter 8: Finding Project Management Insights from A Github Organization
- Chapter 9: Dynamically Optimizing EC2 Instances on AWS
- Chapter 10: Real Estate
- Chapter 11: Production AI for User Generated Content (UGC)
License
This code is released under the MIT license
Text
The text content of notebooks is released under the CC-BY-NC-ND license
Additional Related Topics from Noah Gift
His most recent books are:
- Pragmatic A.I.: An introduction to Cloud-Based Machine Learning (Pearson, 2018)
- Python for DevOps (O'Reilly, 2020).
- Cloud Computing for Data Analysis, 2020
- Testing in Python, 2020
His most recent video courses are:
- Essential Machine Learning and A.I. with Python and Jupyter Notebook LiveLessons (Pearson, 2018)
- AWS Certified Machine Learning-Specialty (ML-S) (Pearson, 2019)
- Python for Data Science Complete Video Course Video Training (Pearson, 2019)
- AWS Certified Big Data - Specialty Complete Video Course and Practice Test Video Training (Pearson, 2019)
- Building A.I. Applications on Google Cloud Platform (Pearson, 2019)
- Pragmatic AI and Machine Learning Core Principles (Pearson, 2019)
- Data Engineering with Python and AWS Lambda (Pearson, 2019)
His most recent online courses are:
