Designing with Machine Learning | Book Chapters

Book Chapter Guide

Current proposed table of content for the designingwithml book is shown below. Note that parts of this may change as the book is developed.
  • Why this book
  • Why Human Centered Design?
  • What you will learn
  • Who is this book for?
  • Who this book is not for
  • Code along and product use cases
  • Acknowledgement
  • 2
    Machine Learning Fundamentals
  • What is machine learning?
  • Artificial Intelligence vs Machine Learning vs Deep Learning
  • Machine learning tasks
  • Evaluation metrics for machine learning models
  • Frameworks and tools for machine learning
  • Machine learning deployment strategies
  • Machine learning application life cycle
  • 3
    Introduction to Tensorflow
  • Why Tensorflow?
  • Implementing a Neuron in Tensorflow
  • Working with data in Tensorflow
  • Building neural networks with Tensorflow
  • Deploying Tensorflow on mobile and edge devices
  • Machine learning in the browser and node.js with Tensorflow.js
  • Tools in the Tensorflow Ecosystem
  • Alternatives to Tensorflow
  • 4
    Your First Machine Learning Application - Taxi Advisor
  • Tooling and Setup
  • Problem framing - An Introduction to Systems Thinking
  • Data collection
  • Building a model
  • Evaluating the model
  • Model deployment
  • Building the user interface
  • Conclusion
  • 5
    What You Need to Know to Build ML Applications
  • Prototyping
  • Framing business problems and machine learning tasks
  • A Taxonomy for selecting the right model
  • Pitfalls for machine learning in production
  • The myth of scale
  • 6
    Building Front End Applications
  • Prototyping a Front End Application
  • Front End Design Guidelines
  • 7
    Building Backend APIs and Modules
  • Building a Backend API
  • Components with Python Packages
  • 8
    Generative AI
  • Overview
  • Why are they called Generative Models?
  • Generative models for text, images, audio and video
  • Limitations of Generative Models
  • Business opportunities for Generative AI
  • Understanding Size Tradeoffs with Generative Models
  • Choosing the Right Generative AI Model
  • Summary
  • Download chapter
    Building with Large Language Models
  • A Cannonical Architecture for LLM Based Applications
  • Techniques for Improving LLM Application Reliability
  • Building with LLM Providers (GPT, PaLM, Claude) and Open Source Models (LLAMA)
  • 10
    Machine Learning and Human Centered Design
  • What is design thinking
  • Why design thinking should never be an afterthought
  • What is DesignOps
  • Machine learning and user experience
  • Guidelines for designing machine learning products
  • ML System Design
  • 11
    Planning Your Machine Learning Project
  • ML project planning
  • Creating a machine learning design document
  • When to use ML?
  • ML in industry
  • Why Machine Learning Projects Fail
  • 12
    Responsible AI
  • Definitions and terminology in responsible AI
  • Pillars of responsible AI
  • Challenges with responsible AI
  • An Oppenheimer moment for AI
  • 13
    Machine Learning Systems Design Use Cases
  • Taxi advisor
  • Automated signature verification
  • Social media dashboard
  • Question answering on large datasets
  • Footer


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