Machine Learning Engineering with Python - Second Edition

In "Machine Learning Engineering with Python, Second Edition," you'll gain the practical MLOps and ML engineering skills you need to address real-world problems effectively. This comprehensive guide offers examples-based learning, enabling you to master the concepts of CI/CD, model lifecycle management, and deployment methodologies, utilizing modern tools like Hugging Face and Ray.

What this Book will help me do

• Understand and manage the machine learning model lifecycle effectively.
• Leverage generative AI and advanced deep learning techniques using tools like PyTorch.
• Set up scalable machine learning solutions with Python and cloud-based technologies.
• Implement automated pipeline orchestration using tools like Apache Airflow and Kubeflow.
• Apply error handling and model monitoring strategies to ensure reliable outcomes.

Author(s)

Andrew P. McMahon is an accomplished machine learning engineer and educator with years of industry experience. As a practitioner in MLOps, Andrew has a proven track record of deploying robust and scalable ML solutions. His engaging teaching style focuses on real-world applications and equipping readers with actionable skills.

Who is it for?

This book is perfect for MLOps and machine learning engineers, data scientists, and software developers eager to handle robust ML systems. It's also valuable for project managers aiming to oversee the lifecycle of ML projects. Prior knowledge of Python and foundational ML concepts is recommended, enabling readers to fully benefit from the examples and insights provided.