Automate Evaluate and Deploy ML Models

Stop letting manual deployments create bottlenecks and introduce risk. Automate, Evaluate and Deploy ML Models Confidently is a hands-on course designed for ML engineers and data scientists ready to master production-grade MLOps. You will move beyond chasing simple accuracy scores and learn to make sophisticated, data-driven decisions by analyzing hyperparameter optimization trials from Optuna, expertly balancing technical performance with critical business KPIs like inference cost and latency. The core of this course is building a complete CI/CD pipeline from the ground up using GitHub Actions. You will integrate MLflow for end-to-end experiment tracking and reproducibility, and implement crucial validation gates that automatically prevent underperforming models from ever reaching production. You will leave this course with a portfolio-ready project that proves you can build, manage, and deploy reliable, automated, and scalable machine learning systems with confidence, bridging the critical gap between experimentation and real-world value. Upon completion, learners are encouraged to deepen their expertise with the "MLOps Specialization" or explore advanced model techniques in the "Deep Learning Specialization"

• Evaluate and analyze model optimization trials using advanced tools
• Build automated CI/CD pipelines for safe and efficient model deployment
• Use MLflow for experiment tracking and ensuring model reproducibility
• Implement automated validation gates to block underperforming models
• Balance technical performance with business KPIs like cost and latency

• Prior hands-on experience with core concepts
• Ability to independently apply main tools or methods