Building and Deploying Machine Learning Services with Docker

Many aspiring Machine Learning engineers struggle to bridge the gap between theoretical models and real-world deployment. This course guides you through the complete lifecycle of a commercial ML project, enabling you to confidently deploy models and demonstrate practical, job-ready skills. What you'll learn: * Understand the complete Machine Learning project lifecycle, from raw data ingestion to service delivery. * Train, evaluate, and serialize a predictive model suitable for production use. * Apply fundamental containerization principles using Docker to package your ML application and dependencies. * Configure a basic API endpoint to serve model predictions effectively. * Practice essential MLOps concepts, including basic model versioning and artifact management. * Develop a high-impact portfolio project demonstrating end-to-end commercial workflow readiness. We start with foundational data preparation and model training, progress through application development, and conclude with the practical steps necessary to containerize and deploy your service. This course is designed for absolute beginners in Machine Learning and Data Science who want to build commercial experience. No prior deployment or Docker experience is required. Start building the portfolio that will accelerate your career today.