Book Recommendations To Grok and Build Machine Learning Systems
Encompassing everything from fundamentals to building production-ready ML systems
“Good friends, good books, and a sleepy conscience: this is the ideal life.”
― Mark Twain
I hope you’re reading this newsletter in your pyjamas looking forward to a rejuvenating and healthy weekend.
So, I have been working on multiple projects ranging from creating MLE/MLOps courses to developing end-to-end ML systems at scale and I have realized that oftentimes, I am either revisiting a book that I’ve read or I’m referring to a book that I just skimmed through but never got the chance to really read it.
This week, I want to share with you the books that I personally feel every ML Enthusiast and Practitioner should read to get a sense of the breadth(ideas) and depth(grok) of this field respectively.
It is a short and crisp list covering the majority of ML topics. It should serve beginners to get started and intermediate-level professionals to understand the intricacies of engineering successful ML systems.
Let’s start turning the pages!
By Aurélien Géron
This book is simply a work of art. I highly recommend not just reading this book but also code along with the author. The book is divided into 2 parts - the first part is focused on the fundamentals of machine learning covering all the major classic ML algorithms with the right amount of mathematical explanation and python code to actually start developing models whereas the second part focuses on neural networks and deep learning.
I have read through this complete book and maybe read a few chapters twice or thrice in order to get the concepts right and do the exercises sometimes.
Reading Tip: Spend 2-3 days(or more if needed) with each chapter if you’re spending 2-3 hours learning actively.
By Andriy Burkov
Andriy Burkov has done it again. This book explains each phase of the ML Systems Lifecycle and is a complete and concise resource for anyone who intends to build scalable ML-powered applications.
The book is a compilation of engineering challenges and best practices to make ML work in production. Andriy has explained how one should look to plan a project, what are the reasons for the failure of a project, and how to approach every step of the pipeline— Before the Project Starts, Data Collection and Preparation, Feature EngineeringSupervised Model Training, Model Evaluation, Model Deployment, Model Serving, Monitoring, and Maintenance.
His first book — The Hundred-Page Machine LearningBook was a great success and the same can be said about this one as well.
The book follows the tenet - Learning by doing. This is a hands-on guide to building Deep Learning applications for the cloud, mobile browsers, and edge devices. I am currently reading this book and I am in shock that I didn’t stumble upon it before.
Every chapter helps you build an application end-to-end. Each application targets a subdomain of deep learning, a different serving method, or techniques to optimize experimentation using TensorFlow.
A must-read for people already familiar with deep learning. This book helps you dive deeper and you learn by building a set of cool projects.
After reading a number of case studies on how organizations like Spotify and Airbnb are using TF Extended to improve their ML platforms, I started learning about TFX and how it makes optimizes the development of end-to-end pipelines.
The book explains techniques to set up ML pipelines right from data ingestion to pipeline orchestration using Airflow or Kubeflow. TFX along with TF offers tools for every step of the process.
This is again an advanced-level read and you must indulge only after you are done reading the top 2 recommendations.
That’s it. I don’t want to bog you down with a plethora of random books. Feel free to reach out by replying to this email if you have any thoughts / recommendations / questions.
Interesting read of the week
This is a slightly unusual recommendation for my newsletter but it fits here because of the sheer quality of the work.
Do you understand how an internal combustion engine works? How all of these parts come together to power your vehicles and machines? I have come across the best possible explanation and illustration of the functionality of all the basic engine parts.
The beautifully designed, 360 degrees, in-action illustrations along with the explanation not only decrypted combustion engines for me but inspired me to work harder on my art.
Reading this made me question whether our education system is doing enough to inspire us or are they just getting away with “teaching” us.