E-Books →Machine Learning Engineering Professional Guide Build 50 Production Models Including MLOps

By Henry Codwell
Master the full machine learning lifecycle-from prototype to production-with this groundbreaking professional guide that delivers unmatched depth, clarity, and hands-on power.
Whether you're a software engineer transitioning into AI, a data scientist ready to productionize your models, or a seasoned ML engineer looking to sharpen your edge, this book is your complete roadmap to building robust, scalable, and reliable machine learning systems that thrive in the real world.
Inside, you'll build 50 production-ready ML models across diverse domains-NLP, computer vision, time-series forecasting, recommendation systems, unsupervised learning, reinforcement learning, and more. Each project is meticulously designed to simulate real-world challenges, giving you the hands-on experience employers and startups demand.
But this isn't just about building models-it's about engineering them. You'll learn how to:
Navigate the full machine learning lifecycle, from data engineering and feature design to monitoring and retraining
Apply MLOps best practices using MLflow, Kubeflow, Docker, FastAPI, and cloud platforms like AWS SageMaker and Google Vertex AI
Optimize and deploy models for real-time inference, scalability, and maintainability
Ensure ethical and responsible AI with explainability, fairness, and privacy-by-design principles
Future-proof your skills with advanced topics including federated learning, model compression, AutoML, and multimodal AI
The book features:
600+ pages of practical content written with clarity and purpose
A companion GitHub repository with all 50 production models, deployment scripts, and datasets
Case studies across industries like finance, healthcare, and retail
A bonus career guide to help you build a standout ML engineering portfolio and prepare for interviews
This isn't a book you read once-it's a professional toolkit you'll return to for years. With a unique blend of depth, practicality, and foresight, Machine Learning Engineering positions you not just to follow trends, but to lead the future of AI.
If you're serious about machine learning in production, this is the book that delivers.