Machine Learning Engineering

By Henry Codwell

Turn your machine learning knowledge into real-world solutions with this comprehensive, project-based guide designed for data scientists, software engineers, and AI practitioners looking to transition from experimentation to production.

This hands-on guide walks you through the development of 50 fully functional machine learning models, covering a wide range of industries and applications—including finance, healthcare, e-commerce, NLP, computer vision, recommendation systems, and time-series forecasting. Each project is engineered to mirror real-world workflows, with an emphasis on scalability, performance, and deployment.

You'll learn to integrate cutting-edge tools such as TensorFlow, Scikit-learn, FastAPI, Docker, Kubernetes, and MLflow into your pipelines, while mastering MLOps practices that ensure reliability, reproducibility, and maintainability of models in production environments.

Key features include:

• End-to-end development of 50 machine learning projects
• Guidance on production-ready model design, training, testing, and deployment
• Step-by-step implementation using Python, with clean, reusable code
• Real-world datasets and scalable architectures
• Coverage of key MLOps tools and CI/CD automation strategies

Whether you're aiming to build your portfolio, advance your career, or deploy robust machine learning systems, this book gives you the practical skills and tools to succeed.

Build smarter. Deploy faster. Master machine learning engineering—purchase your copy of Machine Learning Engineering today and start building production-grade models that deliver real impact.

• Language: English
• Publisher: MARTIN CHAVEZ
• Release date: Jul 21, 2025
• ISBN: 9798231576654

Book preview

Machine Learning Engineering

Professional Guide | Build 50 Production Models | Including MLOps

Henry Codwell

Copyright © 2025

All rights reserved. No portion of Machine Learning Engineering: Professional Guide | Build 50 Production Models | Including MLOps may be reproduced, distributed, or transmitted in any form, including digital, print, or electronic formats, without prior written permission from the copyright holder. This includes, but is not limited to, copying, recording, scanning, or sharing online.

First edition published in 2025

Table of Contents

Preface

Introduction

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Part I: Foundations of Machine Learning Engineering

Chapter 1: Introduction to Machine Learning Engineering

1.1 Defining Machine Learning Engineering

1.2 ML Engineering vs. Data Science vs. Software Engineering

1.3 The Machine Learning Lifecycle

1.4 Importance of Production-Grade ML

1.5 Tools and Tech Stack Overview

Chapter 2: Setting Up Your ML Environment

2.1 Configuring Local and Cloud Environments

2.2 Ensuring Reproducibility: Virtual Environments, Docker, and Git

2.3 Exploring the Book’s GitHub Repository

2.4 Cloud Setup: AWS SageMaker, Google Colab, Azure ML

2.5 Hands-On: Building Your First ML Workspace

Chapter 3: Data Engineering for ML

3.1 Data Sourcing and Preprocessing at Scale

3.2 Handling Imbalanced Datasets, Missing Values, and Outliers

3.3 Feature Engineering Techniques and Automation

3.4 Building Data Pipelines with Apache Airflow and Prefect

3.5 Case Study: Data Pipeline for a Retail Recommendation System

Chapter 4: Model Development Basics

4.1 Overview of ML Algorithms: Supervised, Unsupervised, Reinforcement

4.2 Model Selection and Evaluation Metrics

4.3 Hyperparameter Tuning: GridSearchCV and Bayesian Optimization

4.4 Hands-On: Building a Fraud Detection Classification Model

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Part II: Building Production-Ready Models

Chapter 5: Supervised Learning Models

5.1 Linear Models, Decision Trees, and Ensemble Methods

5.2 Models 1–5: Classification (Churn Prediction, Sentiment Analysis, etc.)

5.3 Models 6–10: Regression (House Price Prediction, Demand Forecasting, etc.)

5.4 Best Practices: Cross-Validation and Model Interpretability

5.5 Hands-On: Implementing an XGBoost Classifier

Chapter 6: Deep Learning Models

6.1 Neural Network Fundamentals: CNNs, RNNs, Transformers

6.2 Models 11–15: Computer Vision (Image Classification, Object Detection with YOLO)

6.3 Models 16–20: NLP (Text Classification, NER with BERT)

6.4 Tools: TensorFlow, PyTorch, Hugging Face Transformers

6.5 Case Study: Deploying a Sentiment Analysis Model with FastAPI

Chapter 7: Time-Series and Forecasting Models

7.1 Time-Series Fundamentals: Stationarity, Seasonality, Trends

7.2 Models 21–25: Classical Models (ARIMA, Prophet)

7.3 Models 26–30: Deep Learning for Time-Series (LSTM, TCN)

7.4 Hands-On: Forecasting Energy Consumption with Prophet and LSTMs

7.5 Best Practices for Time-Series Model Evaluation

Chapter 8: Recommendation Systems

8.1 Collaborative Filtering, Content-Based, and Hybrid Systems

8.2 Models 31–35: Matrix Factorization, Neural Collaborative Filtering

8.3 Tools: Surprise, LightFM, TensorFlow Recommenders

8.4 Case Study: Movie Recommendation System with Implicit Feedback

8.5 Optimizing Recommendation System Performance

Chapter 9: Unsupervised and Reinforcement Learning

9.1 Clustering (K-means, DBSCAN) and Dimensionality Reduction (PCA, t-SNE)

9.2 Models 36–40: Unsupervised (Customer Segmentation, Anomaly Detection)

9.3 Models 41–45: Reinforcement Learning (Q-Learning, DQN for Game AI)

9.4 Hands-On: Anomaly Detection in Network Traffic with Autoencoders

9.5 Challenges and Opportunities in Unsupervised Learning

Chapter 10: Specialized Models

10.1 Models 46–50: Generative AI (GANs), Federated Learning, Graph Neural Networks

10.2 Emerging Trends: Multimodal Models, Tiny ML for Edge Devices

10.3 Case Study: Generating Synthetic Images with Stable Diffusion

10.4 Exploring the Future of Specialized ML Models

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Part III: MLOps and Deployment

Chapter 11: Introduction to MLOps

11.1 What is MLOps? Principles and Workflows

11.2 MLOps Maturity Levels: Manual, Automated, Continuous

11.3 Tools: MLflow, Kubeflow, DVC

11.4 Case Study: MLOps Pipeline for a Churn Prediction Model

11.5 Building a Scalable MLOps Strategy

Chapter 12: Model Deployment

12.1 Deployment Options: Batch, Real-Time, Edge

12.2 Serving Models with Flask, FastAPI, TensorFlow Serving

12.3 Cloud Deployment: AWS SageMaker, GCP Vertex AI, Azure ML

12.4 Hands-On: Deploying a Computer Vision Model with Docker and Kubernetes

12.5 Best Practices for Reliable Deployments

Chapter 13: Model Monitoring and Maintenance

13.1 Monitoring Model Performance: Drift Detection, Accuracy Degradation

13.2 Retraining Pipelines: Triggers and Automation

13.3 Tools: Evidently AI, Prometheus, Grafana

13.4 Case Study: Monitoring a Fraud Detection Model in Production

13.5 Ensuring Long-Term Model Reliability

Chapter 14: Scaling ML Systems

14.1 Distributed Training with Horovod and Ray

14.2 Cost-Efficient Scaling: Spot Instances, Serverless Inference

14.3 Optimizing Latency and Throughput for Real-Time Inference

14.4 Hands-On: Scaling a Recommendation System with AWS Elastic Inference

14.5 Balancing Cost and Performance in ML Systems

Chapter 15: Ethical AI and Governance

15.1 Bias Detection and Mitigation in ML Models

15.2 Explainability with SHAP and LIME

15.3 Compliance: GDPR, CCPA, and AI Regulations

15.4 Case Study: Building a Fair Loan Approval Model

15.5 The Future of Ethical AI

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Part IV: Advanced Topics and Future Trends

Chapter 16: Model Optimization

16.1 Quantization, Pruning, and Knowledge Distillation

16.2 Optimizing for Edge Devices with TensorFlow Lite and ONNX

16.3 Hands-On: Deploying a Lightweight NLP Model on a Raspberry Pi

16.4 Balancing Model Size and Performance

16.5 Advanced Optimization Techniques

Chapter 17: Federated Learning and Privacy

17.1 Federated Learning Fundamentals and Frameworks

17.2 Differential Privacy in Machine Learning

17.3 Case Study: Federated Learning Model for Healthcare

17.4 Privacy-Preserving ML in Production

17.5 Challenges in Federated Learning

Chapter 18: AutoML and Low-Code ML

18.1 AutoML Frameworks: Google AutoML, H2O.ai, DataRobot

18.2 Low-Code Platforms for Rapid Prototyping

18.3 Hands-On: Building a Model with Google AutoML

18.4 When to Use AutoML vs. Custom Models

18.5 The Role of AutoML in Democratizing AI

Chapter 19: Emerging Trends in ML

19.1 Multimodal AI, Self-Supervised Learning, and Foundation Models

19.2 AI for Sustainability and Social Good

19.3 Preparing for Quantum ML and Neuromorphic Computing

19.4 Case Study: Multimodal Model for Image-Text Tasks

19.5 Staying Ahead in the ML Landscape

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Part V: Appendices and Resources

Appendix A: The 50 Production Models

A.1 Summary Table: Domains, Algorithms, Tools, Use Cases

A.2 Links to GitHub Code for Each Model

Appendix B: ML Engineering Toolkit

B.1 Recommended Libraries, Frameworks, and Cloud Services

B.2 Cheat Sheet: ML Algorithms and Evaluation Metrics

Appendix C: Career Guide for ML Engineers

C.1 Building a Portfolio with Production ML Projects

C.2 Preparing for ML Engineering Interviews

C.3 Certifications and Learning Paths

Appendix D: Glossary

- Key Terms in ML Engineering and MLOps

Appendix E: References

- Books, Papers, and Online Resources

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Index

Preface

About This Book

Machine learning has transformed the world in ways that were once the stuff of science fiction. From voice assistants that understand our commands to recommendation systems that seem to know our tastes better than we do, the impact of machine learning is undeniable. Yet, for all its promise, the journey from a theoretical model to a production-grade system is fraught with challenges. This book, Machine Learning Engineering: Professional Guide | Build 50 Production Models | Including MLOps, is born out of a desire to bridge that gap—to provide a comprehensive, practical, and hands-on guide for building machine learning systems that not only work in the lab but thrive in the real world.

The idea for this book came from years of working in the trenches of machine learning engineering, where I’ve seen firsthand the disconnect between academic theory and industrial practice. Too often, aspiring engineers and data scientists master algorithms and frameworks but struggle to deploy models that scale, remain reliable, and deliver value over time. This book is designed to address that problem head-on. It’s not just about understanding machine learning; it’s about engineering machine learning systems that are robust, scalable, and maintainable.

What sets this book apart is its ambitious scope: guiding you through the development of 50 production-grade machine learning models across diverse domains, from natural language processing to computer vision, time-series forecasting to recommendation systems. Each model is a stepping stone, teaching you not just how to code but how to think like a machine learning engineer. You’ll learn to navigate the entire machine learning lifecycle—data preprocessing, model development, deployment, monitoring, and maintenance—while mastering MLOps practices that ensure your systems are production-ready.

This isn’t a theoretical treatise. It’s a hands-on guide packed with Python code, real-world case studies, and a dedicated GitHub repository containing all 50 models. Whether you’re deploying a fraud detection system on AWS or optimizing a lightweight NLP model for a Raspberry Pi, this book provides step-by-step instructions, best practices, and hard-won insights. It also goes beyond the basics, diving into advanced topics like federated learning, ethical AI, and model optimization, ensuring you’re equipped for the future of machine learning.

The structure of the book is deliberate. Part I lays the foundation, covering the tools, workflows, and data engineering skills you need to get started. Part II is the heart of the book, walking you through the 50 production models, each with practical code and real-world applications. Part III dives deep into MLOps, teaching you how to deploy, monitor, and scale your models. Part IV explores cutting-edge topics, from privacy-preserving AI to quantum machine learning, keeping you ahead of the curve. Finally, Part V offers resources like a career guide, a glossary, and a comprehensive toolkit to support your journey as an ML engineer.

My goal was to create a book that feels like a mentor, not just a manual. Every chapter is written with the clarity and depth I wish I’d had when I started my career. The tone is practical but engaging, balancing technical rigor with real-world context. You’ll find case studies drawn from industries like retail, healthcare, and finance, showing how machine learning solves real problems. You’ll also find a focus on ethical considerations, because building AI isn’t just about what we can do—it’s about what we should do.

This book is designed to compete with the best-selling machine learning books on Amazon, but it aims to surpass them by offering more depth, more practicality, and more relevance to today’s fast-evolving field. Whether you’re a programmer transitioning to ML, a data scientist aiming to productionize your models, or an experienced engineer looking to master MLOps, this book is your roadmap. It’s not just about learning machine learning—it’s about becoming a machine learning engineer who builds systems that matter.

Who Should Read This Book

Machine learning is a field that attracts a wide range of professionals, from curious coders to seasoned researchers. But not every machine learning book is for everyone. Machine Learning Engineering: Professional Guide | Build 50 Production Models | Including MLOps is written for a specific audience: those who want to go beyond theory and build machine learning systems that work in the real world. If you’re wondering whether this book is for you, let me paint a picture of its ideal reader.

First and foremost, this book is for programmers and software engineers who want to transition into machine learning engineering. You might be fluent in Python, Java, or C++, and you’ve built software systems that scale. But now you’re intrigued by machine learning and want to apply your skills to AI. You don’t just want to train models—you want to deploy them, monitor them, and ensure they perform reliably in production. This book assumes you have basic programming skills and some familiarity with machine learning concepts (like supervised learning or neural networks), but it doesn’t require you to be an expert. It will guide you through the nuances of ML engineering, from data pipelines to cloud deployment, with clear explanations and practical code.

Data scientists are another key audience. If you’ve spent time building models with Scikit-learn, TensorFlow, or PyTorch, and you’re comfortable with concepts like cross-validation or hyperparameter tuning, this book will take you to the next level. Many data scientists excel at prototyping but struggle with productionizing their work. This book teaches you how to turn your Jupyter notebook experiments into robust, scalable systems. You’ll learn MLOps practices, deployment strategies, and monitoring techniques that ensure your models don’t just perform well in a lab but deliver value in real-world applications.

This book is also for machine learning engineers who want to deepen their expertise. If you’re already deploying models but want to master advanced topics like federated learning, model optimization, or ethical AI, this book has you covered. The 50 production models span a wide range of domains and complexities, offering challenges even for seasoned professionals. You’ll find detailed case studies, advanced MLOps workflows, and emerging trends that keep you at the forefront of the field.

Students and educators in computer science or data science programs will find this book valuable as well. If you’re studying machine learning and want a practical complement to your coursework, this book provides hands-on projects that bring theory to life. Professors can use the 50 models as assignments or inspiration for capstone projects, with the GitHub repository offering ready-to-use code. The book’s clear structure and comprehensive coverage make it a strong resource for both self-study and classroom use.

Finally, this book is for anyone curious about building production-grade AI systems. You might be a tech enthusiast, a startup founder, or a manager overseeing an AI team. While the book is technical, its real-world focus and clear explanations make it accessible to motivated learners who are willing to roll up their sleeves. If you’re excited about the idea of building a recommendation system for a retail platform or deploying a computer vision model on the cloud, this book will show you how.

What you won’t find here is a beginner’s introduction to machine learning. If terms like gradient descent or overfitting are unfamiliar, you might want to start with an introductory text before diving in. Similarly, this book isn’t a deep dive into machine learning theory—it’s about engineering, not research. But if you’re ready to build, deploy, and maintain machine learning systems that solve real problems, you’re in the right place. This book is your guide to becoming a machine learning engineer who doesn’t just understand AI but shapes it.

How to Use This Book

Machine Learning Engineering: Professional Guide | Build 50 Production Models | Including MLOps is designed to be both a comprehensive guide and a practical workbook. With 600 pages, 50 production models, and a wealth of code and case studies, it’s a substantial resource that can feel overwhelming at first. But don’t worry—my goal is to make this book as approachable as it is thorough. Here’s how to get the most out of it, whether you’re reading cover to cover or dipping into specific sections.

The book is structured in five parts, each building on the previous one. Part I, Foundations of Machine Learning Engineering, is your starting point. It covers the essentials: setting up your environment, handling data, and building basic models. If you’re new to ML engineering or need a refresher, read these chapters carefully. They lay the groundwork for everything that follows. Even if you’re experienced, skim them to familiarize yourself with the book’s tools and workflows, like the GitHub repository and cloud setups.

Part II, Building Production-Ready Models, is the core of the book. It guides you through 50 production models across domains like NLP, computer vision, and recommendation systems. Each model is a self-contained project, complete with Python code, explanations, and real-world applications. You don’t have to build all 50 models in order, but I recommend working through at least one model per chapter to get a feel for the process. For example, try the fraud detection classifier in Chapter 5 or the sentiment analysis model in Chapter 6. The models increase in complexity, so start with earlier ones if you’re new to a domain. Use the GitHub repository to access the code and follow along with the hands-on exercises.

Part III, MLOps and Deployment, is where you’ll learn to productionize your models. This section is critical for anyone aiming to deploy ML systems in the real world. Read it sequentially to understand the full MLOps lifecycle—deployment, monitoring, scaling, and governance. Each chapter includes a case study, like deploying a computer vision model with Kubernetes or monitoring a fraud detection system. If you’re short on time, prioritize the hands-on sections to build practical skills.

Part IV, Advanced Topics and Future Trends, dives into cutting-edge areas like federated learning, model optimization, and multimodal AI. This section is ideal for experienced readers or those curious about the future of ML. You can read these chapters selectively based on your interests, but I encourage everyone to explore the ethical AI chapter, as it’s relevant to all ML engineers. The hands-on projects, like deploying a model on a Raspberry Pi, are great for building portfolio-worthy work.

Part V, Appendices and Resources, is your reference section. It includes a summary of the 50 models, a toolkit of libraries and frameworks, and a career guide for ML engineers. Use the glossary to clarify terms and the references to dive deeper into specific topics. The career guide is especially useful if you’re job-hunting or planning to showcase your skills.

To use this book effectively, treat it as a hands-on journey. You’ll need a computer with Python installed, ideally with access to a cloud platform like AWS, GCP, or Azure. Clone the GitHub repository early—it contains all the code, datasets, and scripts you’ll need. Each chapter includes QR codes linking to Jupyter notebooks for interactive exercises, so keep a QR code scanner handy. If you’re new to a topic, read the explanations first, then run the code. If you’re experienced, jump to the hands-on sections and adapt the code to your own projects.

You can read the book linearly, but it’s also designed for modular use. If you’re focused on NLP, start with Chapter 6. If MLOps is your goal, jump to Part III. The index and table of contents make it easy to find specific topics. I recommend setting aside time to work through at least one model per week, as the hands-on practice is where the real learning happens. If you get stuck, the companion website offers tutorials, errata, and community support.

A few tips: take notes as you read, especially on tools and workflows. Experiment with the code—modify parameters, try different datasets, or deploy on a different cloud platform. The case studies are drawn from real-world scenarios, so think about how they apply to your own projects. Finally, don’t skip the ethical AI sections. As ML engineers, we have a responsibility to build systems that are fair and transparent.

This book is your partner in becoming a machine learning engineer. It’s dense, but it’s designed to grow with you. Whether you’re building your first model or your fiftieth, you’ll find insights and tools to take your skills further. Dive in, get your hands dirty, and let’s build something extraordinary.

Acknowledgments

Writing a book