Machine Learning Engineering

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.