Uploaded June 2021 | Updated September 2026, 2 weeks ago
MLOps is growing very fast today. Every data scientist should know where Data Science is and how it is transforming today. The fields of MLOps AI Engineering are dramatically changing and demand of specialists in these domains are growing every day.
Recently Google released whitepapers dedicated MLOps (Machine Learning Operations), you can find it here: services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf
That means that MLOps domain will fill the important gap between Data Science, Machine Learning and Data Engineering.
I have explored many sources where I could get started with MLOps. Some materials are good, but lack real life examples, some others look so narrow. I found a beautiful book which I am still reading and using in my daily work as a Data Scientist, and can recommend to you.
It is: Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale. - written by Emmanuel Raj in 2021.
The structure of the book:
Section 1 - Framework for Building Machine Learning Models
Section 2 - Deploying Machine Learning at Scale
Section 3 - Monitoring Machine Learning Models in Production
0:00 - Intro. What is MLOps? Where to start?
2:29 - About the Author - Emmanuel Raj
3:36 - Chapter 1 - Fundamentals of an MLOps Workflow
4:17 - Chapter 2 - Characterizing Your Machine Learning Problem
5:15 - Chapter 3 - Code Meets Data
6:13 - Chapter 4 - Machine Learning Pipelines
7:11 - Chapter 5 - Model Evaluation and Packaging
7:52 - Chapter 6 - Key Principles for Deploying Your ML System
9:07 - Chapter 7 - Building Robust CI-CD Pipelines
10:00 - Chapter 8 - APIs and Microservice Management
10:55 - Chapter 9 - Testing and Securing Your ML Solution
12:10 - Chapter 10 - Essentials of Production Release
13:10 - Chapter 11 - Key Principles for Monitoring Your ML System
14:05 - Chapter 12 - Model Serving and Monitoring
15:00 - Chapter 13 - Governing the ML System for Continual Learning
15:54 - Final word and conclusions.
MLOps is a systematic approach to building, deploying, and monitoring Machine Learning solutions. It is an engineering discipline that can be applied to various industries and use cases. This book presents comprehensive insights into MLOps coupled with real-world examples to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production.
By the end of this machine learning book, you will have a 360 degree view of MLOps and be ready to implement MLOps in your organization or business.
Link to the Amazon page to get official description: amazon.com/Engineering-MLOps-Rapidly-production-ready-learning/dp/1800562888
Link to author - Emmanuel Raj LiinkedIn: linkedin.com/in/emmanuelraj7
Link to the article providing more information about MLOps technologies diverging in USA and China and other actual news on MLOps: huyenchip.com/2020/12/30/mlops-v2.html
(Video) Emmanuel Raj - Speaker Sourcing Contest: youtube.com/watch?v=jUjt06pKmII
I think this book must be in every Data Scientist or Machine Learning Engineer hands to be even better specialist and build a magical things in Data Science.
#mlops #machinelearningengineer #mlbook
MLOps is growing very fast today. Every data scientist should know where Data Science is and how it is transforming today. The fields of MLOps AI Engineering are dramatically changing and demand of specialists in these domains are growing every day.
Recently Google released whitepapers dedicated MLOps (Machine Learning Operations), you can find it here: services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf
That means that MLOps domain will fill the important gap between Data Science, Machine Learning and Data Engineering.
I have explored many sources where I could get started with MLOps. Some materials are good, but lack real life examples, some others look so narrow. I found a beautiful book which I am still reading and using in my daily work as a Data Scientist, and can recommend to you.
It is: Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale. - written by Emmanuel Raj in 2021.
The structure of the book:
Section 1 - Framework for Building Machine Learning Models
Section 2 - Deploying Machine Learning at Scale
Section 3 - Monitoring Machine Learning Models in Production
0:00 - Intro. What is MLOps? Where to start?
2:29 - About the Author - Emmanuel Raj
3:36 - Chapter 1 - Fundamentals of an MLOps Workflow
4:17 - Chapter 2 - Characterizing Your Machine Learning Problem
5:15 - Chapter 3 - Code Meets Data
6:13 - Chapter 4 - Machine Learning Pipelines
7:11 - Chapter 5 - Model Evaluation and Packaging
7:52 - Chapter 6 - Key Principles for Deploying Your ML System
9:07 - Chapter 7 - Building Robust CI-CD Pipelines
10:00 - Chapter 8 - APIs and Microservice Management
10:55 - Chapter 9 - Testing and Securing Your ML Solution
12:10 - Chapter 10 - Essentials of Production Release
13:10 - Chapter 11 - Key Principles for Monitoring Your ML System
14:05 - Chapter 12 - Model Serving and Monitoring
15:00 - Chapter 13 - Governing the ML System for Continual Learning
15:54 - Final word and conclusions.
MLOps is a systematic approach to building, deploying, and monitoring Machine Learning solutions. It is an engineering discipline that can be applied to various industries and use cases. This book presents comprehensive insights into MLOps coupled with real-world examples to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production.
By the end of this machine learning book, you will have a 360 degree view of MLOps and be ready to implement MLOps in your organization or business.
Link to the Amazon page to get official description: amazon.com/Engineering-MLOps-Rapidly-production-ready-learning/dp/1800562888
Link to author - Emmanuel Raj LiinkedIn: linkedin.com/in/emmanuelraj7
Link to the article providing more information about MLOps technologies diverging in USA and China and other actual news on MLOps: huyenchip.com/2020/12/30/mlops-v2.html
(Video) Emmanuel Raj - Speaker Sourcing Contest: youtube.com/watch?v=jUjt06pKmII
I think this book must be in every Data Scientist or Machine Learning Engineer hands to be even better specialist and build a magical things in Data Science.
#mlops #machinelearningengineer #mlbook
