Machine Learning Engineering with Python
Manage the lifecycle of machine learning models using MLOps with practical examples
Created by Andrew P. McMahon
Gain practical skills for managing the full lifecycle of machine learning models, from development to deployment. Learn how to standardize pipelines, handle real-world challenges, and use modern MLOps tools to deliver reliable, scalable solutions. Explore deep learning, generative AI, and cloud-based workflows to stay ahead in the field.
Packt | Aug 2023 | 462 min
What You Will Learn
You will work through real-world scenarios using step-by-step explanations and practical examples. Each topic builds on hands-on projects that guide you through planning, building, and deploying machine learning solutions. By applying modern tools and techniques, you will gain confidence in handling the technical and operational aspects of ML engineering.
Key Features
- Design and manage robust ML pipelines for scalable production workflows
- Apply MLOps best practices to monitor models and automate retraining
- Leverage cloud and open-source tools to deploy and maintain ML microservices
Target Audience
Ideal for MLOps engineers, ML engineers, data scientists, and software developers with a basic understanding of machine learning and intermediate Python skills. If you want to build, deploy, or manage robust machine learning systems in production, or gain a deeper understanding of the ML lifecycle, you will benefit from these practical, actionable skills.
