Clean Machine Learning Code
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.
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.
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!
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.
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.
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!
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.
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
Chapter 2 - Optimizing Names
- 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
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
Chapter 4 - Style
- 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
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
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
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
- 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
You reforest the worldhttps://tree-nation.com
Tree-Nation is the largest reforestation platform enabling citizens and companies to plant trees around the world.
The Leanpub 45-day 100% Happiness Guarantee
Within 45 days of purchase you can get a 100% refund on any Leanpub purchase, in two clicks.
See full terms
Free Updates. DRM Free.
If you buy a Leanpub book, you get free updates for as long as the author updates the book! Many authors use Leanpub to publish their books in-progress, while they are writing them. All readers get free updates, regardless of when they bought the book or how much they paid (including free).
Most Leanpub books are available in PDF (for computers), EPUB (for phones and tablets) and MOBI (for Kindle). The formats that a book includes are shown at the top right corner of this page.
Finally, Leanpub books don't have any DRM copy-protection nonsense, so you can easily read them on any supported device.
C++ Best PracticesJason Turner
Level up your C++, get the tools working for you, eliminate common problems, and move on to more exciting things!
Continuous Delivery PipelinesDave Farley
This practical handbook provides a step-by-step guide for you to get the best continuous delivery pipeline for your software.
OpenIntro StatisticsDavid Diez, Christopher Barr, Mine Cetinkaya-Rundel, and OpenIntro
A complete foundation for Statistics, also serving as a foundation for Data Science.
Leanpub revenue supports OpenIntro (US-based nonprofit) so we can provide free desk copies to teachers interested in using OpenIntro Statistics in the classroom and expand the project to support free textbooks in other subjects.
More resources: openintro.org.
C++20 is the next big C++ standard after C++11. As C++11 did it, C++20 changes the way we program modern C++. This change is, in particular, due to the big four of C++20: ranges, coroutines, concepts, and modules.
The book is almost daily updated. These incremental updates ease my interaction with the proofreaders.
Atomic KotlinBruce Eckel and Svetlana Isakova
For both beginning and experienced programmers! From the author of the multi-award-winning Thinking in C++ and Thinking in Java together with a member of the Kotlin language team comes a book that breaks the concepts into small, easy-to-digest "atoms," along with exercises supported by hints and solutions directly inside IntelliJ IDEA!
Introductory Statistics with Randomization and SimulationMine Cetinkaya-Rundel, Christopher Barr, OpenIntro, and David Diez
A complete foundation for Statistics, also serving as a foundation for Data Science, that introduces inference using randomization and simulation while covering traditional methods.
Leanpub revenue supports OpenIntro, so we can provide free desk copies to teachers interested in using our books in the classroom.
More resources: openintro.org.
Ansible for DevOpsJeff Geerling
Ansible is a simple, but powerful, server and configuration management tool. Learn to use Ansible effectively, whether you manage one server—or thousands.
Java OOP Done RightAlan Mellor
Object Oriented Programming is still a great way to create clean, maintainable code. But only if you use it right.
This book gives you 25 years of OO best practice, ready to use.
You'll learn to design objects behaviour-first, use TDD to help, then confidently apply Design Patterns, SOLID principles and Refactoring to make clean, crafted code.
Introducing EventStormingAlberto Brandolini
The deepest tutorial and explanation about EventStorming, straight from the inventor.
Discrete Mathematics for Computer ScienceAlexander Shen, Alexander S. Kulikov, Vladimir Podolskii, and Aleksandr Golovnev
This book supplements the DM for CS Specialization at Coursera and contains many interactive puzzles, autograded quizzes, and code snippets. They are intended to help you to discover important ideas in discrete mathematics on your own. By purchasing the book, you will get all updates of the book free of charge when they are released.
Software Architecture for Developers: Volumes 1 & 2 - Technical leadership and communication
2 Books"Software Architecture for Developers" is a practical and pragmatic guide to modern, lightweight software architecture, specifically aimed at developers. You'll learn:The essence of software architecture.Why the software architecture role should include coding, coaching and collaboration.The things that you really need to think about before...
CCIE Service Provider Ultimate Study Bundle
2 BooksPiotr Jablonski, Lukasz Bromirski, and Nick Russo have joined forces to deliver the only CCIE Service Provider training resource you'll ever need. This bundle contains a detailed and challenging collection of workbook labs, plus an extensively detailed technical reference guide. All of us have earned the CCIE Service Provider certification...
Cisco CCNA 200-301 Complet
4 BooksCe lot comprend les quatre volumes du guide préparation à l'examen de certification Cisco CCNA 200-301.
Modern C++ by Nicolai Josuttis
CCDE Practical Studies (All labs)
3 BooksCCDE lab
"The C++ Standard Library" and "Concurrency with Modern C++"
2 BooksGet my books "The C++ Standard Library" and "Concurrency with Modern C++" in a bundle. The first book gives you the details you should know about the C++ standard library; the second one dives deeper into concurrency with modern C++. In sum, you get more than 600 pages full of modern C++ and about 250 source files presenting the standard library...
2 BooksDocker and Kubernetes are taking the world by storm! These books will get you up-to-speed fast! Docker Deep Dive is over 400 pages long, and covers all objectives on the Docker Certified Associate exam.The Kubernetes Book includes everything you need to get up and running with Kubernetes!
Modern Management Made Easy
3 BooksRead all three Modern Management Made Easy books. Learn to manage yourself, lead and serve others, and lead the organization.
The Future of Digital Health
6 BooksWe put together the most popular books from The Medical Futurist to provide a clear picture about the major trends shaping the future of medicine and healthcare. Digital health technologies, artificial intelligence, the future of 20 medical specialties, big pharma, data privacy and how technology giants such as Amazon or Google want to conquer...
Django for Beginners/APIs/Professionals