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Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples
Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environments Machine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services. Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems. By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering. This book is for machine learning engineers, data scientists, and software developers who want to build robust software solutions with machine learning components. If you're someone who manages or wants to understand the production life cycle of these systems, you'll find this book useful. Intermediate-level knowledge of Python is necessary.
276 pages, Paperback
Published November 5, 2021
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Displaying 1 - 2 of 2 reviews
March 26, 2022
This book is mostly for Machine Learning specialist that want to be more actively (and useful) contributors to the projects. The full Data Science pipeline is considered, from data ingestion to design and deployment and the solution.
Way too often, Data Science projects failed or are delayed because of the difficult communications between people caring about the model training and little to the business value or the deployment.
This book help filling this gap by training the read to properly design the solution upfront, how to split the task inside the team, how to efficiently delivered and deploy on cloud. While the focus is on AWS, the recipes can be easily transferred to other cloud solutions.
I particularly appreciated the global view of Data Science and normal software development practices. Way too often I saw that Agile methodologies (or Lean, like Kanban) can not be applied to Data Science. This book proves otherwise. Similarly, there are references about versioning the models. The exposition is not fully exhaustive (and I doubt this is actually possible): agile methodologies are referenced quickly, as well as git and MLflow are not extensively illustrated. This would be detrimental for the book, that would become an arid, boring and verbose treaty.
With this book, you get a general framework to introduce model software engineering best practices in the pipeline. With the two use cases presented in depth as the last two chapters, the author manage to provide a pragmatic, synthetic view. The use cases are relevant, and not abstract, academia-like projects.
The reading is quite pleasant. But, most importantly, easy to implement for quick improvement in Machine Learning Engineering.
Way too often, Data Science projects failed or are delayed because of the difficult communications between people caring about the model training and little to the business value or the deployment.
This book help filling this gap by training the read to properly design the solution upfront, how to split the task inside the team, how to efficiently delivered and deploy on cloud. While the focus is on AWS, the recipes can be easily transferred to other cloud solutions.
I particularly appreciated the global view of Data Science and normal software development practices. Way too often I saw that Agile methodologies (or Lean, like Kanban) can not be applied to Data Science. This book proves otherwise. Similarly, there are references about versioning the models. The exposition is not fully exhaustive (and I doubt this is actually possible): agile methodologies are referenced quickly, as well as git and MLflow are not extensively illustrated. This would be detrimental for the book, that would become an arid, boring and verbose treaty.
With this book, you get a general framework to introduce model software engineering best practices in the pipeline. With the two use cases presented in depth as the last two chapters, the author manage to provide a pragmatic, synthetic view. The use cases are relevant, and not abstract, academia-like projects.
The reading is quite pleasant. But, most importantly, easy to implement for quick improvement in Machine Learning Engineering.
December 25, 2023
Very good introduction to ML. Assumes you know Python, Apache Airflow, Aws and Jira -- these are some of the tools used.
In ~250 page, found the information concise and relevant to ML.
In ~250 page, found the information concise and relevant to ML.
Displaying 1 - 2 of 2 reviews