The Full-Stack ML Engineer
From Data to Production: Master Machine Learning at Every Layer
Created by IraklisBuild production-grade machine learning systems that scale, perform, and adapt in real-world environments. This comprehensive guide takes you from raw data to deployed models, covering every layer of the full-stack ML engineering process. You'll master data preprocessing pipelines that ensure quality inputs, implement feature engineering methods that unlock predictive power, and design neural network architectures suited to your problems. Learn how to evaluate models rigorously using the right metrics, optimize hyperparameters systematically, and deploy with confidence using containerization and cloud platforms. Discover MLOps best practices that automate workflows, monitor performance in production, and enable continuous improvement through A/B testing. Whether you're building recommendation systems, natural language processing applications, or computer vision solutions, you'll gain the architectural thinking and practical skills to handle scalability, interpretability, and reliability. This book bridges the gap between ML theory and production reality, equipping you with frameworks, tools, and patterns used by leading ML engineers at scale.
Est. Length
222 pages (11 chapters)
Language
English
Publication date
Jan 11, 2026
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