Clean Machine Learning Code
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Clean Machine Learning Code

About the Book

It is super clear that people involved with ML/DS type of work, are really really smart.

Individuals in this field know about statistics, machine learning, academic research, data manipulation, and they excel at reasoning, and logical thinking beyond belief.

However, as these talented individuals transition to working with software engineers and product managers, on live products, they notice a deep sense of instability.

The reality is that software is an intensely creative activity but produces tremendously fragile artifacts.

Now, if traditional software products are fragile and can be taken down by flipping a single IF condition at the right place, one can only imagine the fragility of ML software.

How can something so valuable for company profits, and society as a whole be so fragile? And how can this fragility be so unexplored by ML practitioners?

Not into reading? Checkout the companion 5 hours video course + book bundle:

If you read this far, you are well aware that there is no useful Machine Learning (ML) without extensive software. But building complex software comes with many challenges.

ML software is explicitly full of needless complexity and repetition. Thick opacity, rigidity, and viscosity of design magnify this brew of complexity. With these issues, ML failures are growing in importance at an unprecedented pace.

It does not have to be this way.

As a global data science community, the autonomous systems we build can be costly, dangerous, and even deadly. Adding to the problem is the inexperienced workforce of this 5 to 10 years old craft. As of 2019-2020, 40% of data scientists in the USA have less than 5 years of experience.

The software industry is experiencing a boom in ML development and usage. This is not unlike previous software engineering booms in the early 2000s. The current boom manifests itself with a menagerie of constructs, abstractions, frameworks, and workflows. This multitude of integration challenges remind us of old and classical software problems. Some of the issues present in the ML software engineering practice are new. But the majority of the software engineering concerns have a historical smell. Going back to the early days of software engineering can help with today’s ML problems.

For us, ML engineers, it is time to stop reinventing the wheel, making the same old mistakes, and start using the decades of successful software engineering practices by replacing “Software” with “Machine Learning Software”.

This book can help with that.

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  • Categories

    • Machine Learning
    • Software Engineering
    • Python
    • Artificial Intelligence
    • Software Architecture

About the Author

Moussa Taifi
Moussa Taifi

Data science platform architect focused on data science productivity, reliability, performance, and cost.

Working on designing and implementing large scale AI products through data collection, analysis, and warehousing.

Passionate about building scalable machine learning pipeline architectures with high business impact.

Aspiring author.



Reader Testimonials

Adam Petranovich
Adam Petranovich

Chief Data Scientist Prognos Health

Whether you are new to the Data Science/ Software engineering field or a seasoned expert, Clean Machine Learning Code has a place on your shelf. Dr. Taifi goes beyond the 'what' to do, and explores why you should do things. I found his book clear, concise and an immediate order for my team.

Tian Yu
Tian Yu

Applied Scientist at Amazon

Clean Machine Learning Code is a great coding style guidance that walks you through end-to-end good coding habits from variable naming to architecture and test, along with a ton of easy to understand examples. This book should be recommended to every Machine Learning and Data Science practitioner!

Ben Robinson
Ben Robinson

Data Scientist at Aetna

Too often in data science, considerations around architecture and pipeline optimization are treated as secondary to the machine learning problem. This book shows why that's a mistake and offers data scientists a clear guide on how to avoid it.

Ryan Woodard
Ryan Woodard

Staff Data Scientist at Thorn

In the same way that Lebowski's rug tied his room together, the organized knowledge and best practices in this book tie together the random bits of informal learning, online courses, self-study and pseudo-software engineering that populate the brains of most data scientists, who arrived at their posts through unplanned random walks unladen by nonexistent industry pedagogy. This book is great and is helping me re-evaluate parts of my workflow and style, making them more efficient.

Daniel Austin
Daniel Austin

Senior Data Scientist at Nike

A comprehensive (and often humorous) tour of how to build ML systems that can be deployed and maintained in production and at scale. These lessons are often learned at high cost on the job if at all. I cannot recommend this book enough!

Win Suen
Win Suen

Machine Learning Engineer

I enjoyed reading Moussa Taifi's work, Clean Machine Learning Code. The book is centered around solid software development best practices, but never loses sight of the ML practitioner, who often negotiates a delicate balance between technical and business needs. It is also written with a sense of humor and a very conversational tone. You feel like you are sitting down to coffee with a good friend who will tell it to you straight about how your ML practice can be improved.

Table of Contents

  • Chapter 1 - Clean Machine Learning Code Fundamentals
    • The Flowchart: Why You Need This Book
    • The Future of Machine Learning Code
    • Bad Machine Learning Code
    • The TCO of a Predictive Service Mess
    • Rebuild the ML Pipelines from Scratch
    • Ideal vs. Real Machine Learning Workflows
    • Taking Responsibility for ML Code Rot
    • Overfitting to Deadlines
    • The Art of Feature Engineering Your Code
    • What Is Clean Machine Learning Code?
    • Inference vs Training of Source Code
    • Active Reinforcement Learning for Source Code
    • Transfer Learning and the Origins of CMLC
    • Conclusion
    • References
  • Chapter 2 - Optimizing Names
    • Introduction
    • The Objective Function of Names
    • Avoid Mislabeled Labels
    • Avoid Noisy Labels
    • Make Siri Say it
    • Make it Greppable
    • Avoid Name Embeddings
    • Avoid Semantic Name Maps
    • Part-of-Speech Tagging
    • CumSum vs. CummulativeSum
    • Naming Consistency
    • Avoid Paronomasia
    • Use Technical Names
    • Use Domain Names
    • Use Clustering for Context
    • The Scope Length Guidelines
    • Conclusion
    • References
  • Chapter 3 - Optimizing Functions
    • Small is Beautiful
    • 3, 4, maybe 5 lines max!
    • Hierarchical functions
    • Single Objective Function
    • Bagging and Function Ensembles
    • Single Abstraction Level
    • Function Arguments
    • Have No Collateral Damage
    • Side-effects in Feature Engineering Pipelines
    • Functional Programming 101
    • Make Temporal Couplings Explicit
    • Grokking Commands vs. Queries
    • Handling Exceptions
    • Single Entry, Single Exit
    • A Method to the Madness
    • Conclusion
    • References
  • Chapter 4 - Style
    • Comments
    • Don’t Hide Bad Code Behind Comments
    • Let Code Explain Itself
    • Useful comments
    • Useless Comments
    • Formatting Goals
    • Python File Size and Notebook Size
    • PEP-8 When You Can
    • Minimize Conceptual Distances
    • One last thing about one-liners
    • Conclusion
    • References
  • Chapter 5 - Clean Machine Learning Classes
    • I Know Classes in Python Why Are You Wasting My Time?
    • Goals for ML Class Design
    • S.O.L.I.D Design Principles for ML Classes
    • Small Cohesive Classes: The Single Responsibility Principle
    • Organizing for Change: The Open-Closed Principle
    • Maintaining Contracts: The Liskov Substitution Principle
    • Isolating from Change I: The Interface Substitution Principle
    • Isolating from Change II: The Dependency Inversion Principle
    • Conclusion
    • References
  • Chapter 6 - ML Software Architecture
    • The purpose of ML Software Architecture
    • Third-party packages are NOT an Architecture
    • Architecture is about Usage
    • Avoiding Chaos using Architecture
    • Frameworks and Harems
    • Defining ML Use-cases
    • Separating High Level Policy from Low Level Implementation
    • The Clean Architecture in One Picture
    • Related Architecture Names and Concepts
    • Friction and Boundary Conditions
    • Taming the Recsys Beast
    • Clean ML Architecture
    • Re-architecting the ML Pipeline
    • Living with a Main
    • Conclusion
    • References
  • Chapter 7 - Test Driven Machine Learning
    • Making Your Life Harder in the Short Term
    • 60 Minutes to Save Lives
    • Does ML Code Rot?
    • Tests Let You Clean Your Code
    • Self-testing ML Code
    • What is this TDD you are talking about?
    • Which ML Code Tests Do You Need?
    • GridSearch for ML Code Tests
    • Unit Tests
    • Integration Tests
    • Component Tests
    • End-to-End Tests
    • Threshold Tests
    • Regression Tests
    • Test Implementation techniques
    • Test Doubles
    • Cost Effective Tests
    • Property-based testing
    • Exterminate Non-Determinism in ML Tests
    • The Basics
    • Social Distancing
    • Isolation And Co-mingling
    • The Brave New Async World
    • Working around Remote Services
    • Clocks
    • It Only Fails During Business Hours
    • Test Coverage
    • What To Do If You Are Giving Up on Testing
    • Testing Expeditions a.k.a. Exploratory Testing
    • Synthetic Monitoring
    • Feature Toggles
    • Approaches From Around The ML Community
    • Software 2.0
    • The ML Test Score
    • ML Score Checklist Visualized
    • Coding Habits for Data Scientists
    • Continuous Delivery
    • Conclusions
    • References

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