Machine Learning Engineering

<p>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.</p><p>This hands-on guide walks you through the development of <strong>50 fully functional machine learning models</strong> 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.</p><p>You'll learn to integrate cutting-edge tools such as TensorFlow Scikit-learn FastAPI Docker Kubernetes and MLflow into your pipelines while mastering <strong>MLOps practices</strong> that ensure reliability reproducibility and maintainability of models in production environments.</p><p>Key features include:</p><ul><li>End-to-end development of 50 machine learning projects</li><li>Guidance on production-ready model design training testing and deployment</li><li>Step-by-step implementation using Python with clean reusable code</li><li>Real-world datasets and scalable architectures</li><li>Coverage of key MLOps tools and CI/CD automation strategies</li></ul><p>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.</p>