Machine Learning Engineering with Python

Machine Learning Engineering with Python is a practical guide that helps you learn how to manage the entire lifecycle of machine learning projects using Python and modern MLOps practices. Through its step-by-step examples and clear explanations, you will acquire the skills needed to deploy scalable and robust machine learning solutions.

What this Book will help me do

• Learn how to structure and automate the training and deployment of ML models, enhancing efficiency.
• Develop your own Python libraries and packages to encapsulate and extend ML capabilities.
• Master deployment patterns like microservices for delivering machine learning insights to production environments.
• Gain clarity on using cloud-based tools effectively for training and deployment at scale.
• Understand how to organize, manage, and standardize processes to ensure reliable, reproducible results in ML engineering.

Author(s)

Andrew P. McMahon is a passionate expert in machine learning engineering with a deep knowledge in Python and MLOps methodologies. With years of experience both developing machine learning models and deploying them in production at scale, Andrew focuses on sharing actionable insights and practical advice for professionals in the field. His clear and thoughtful approach makes complex topics accessible.

Who is it for?

This book is made for machine learning engineers, data scientists, and software developers looking to productionize machine learning solutions. If you have an intermediate knowledge of Python and are comfortable with software development, this book will help you learn best practices for managing the machine learning lifecycle at scale. It is suitable both for practitioners new to ML engineering and experienced developers seeking to refine their processes.