Deploy, Evaluate, and Create AI Systems

Course Description: Deploy, Evaluate, and Create AI Systems Did you know that nearly 70% of AI models never make it to production due to deployment issues like version conflicts, poor scaling, and downtime during updates? Reliable deployment is the key to transforming prototypes into production-grade AI systems. This Short Course was created to help ML and AI professionals deploy AI systems reliably in production, optimize deployment costs and performance, and implement zero-downtime release strategies for mission-critical AI services. By completing this course, you will be able to analyze, evaluate, and create scalable AI deployment pipelines using containerization, cloud orchestration, and blue-green deployment methods—skills you can immediately apply to ensure seamless, high-performance model releases. By the end of this course, you will be able to: • Analyze dependency graphs and container configurations to detect version conflicts. • Evaluate performance, latency, and cost metrics across deployment targets. • Create a blue-green deployment strategy for zero-downtime model upgrades. This course is unique because it blends DevOps principles with AI engineering, giving you practical experience in managing version control, optimizing system performance, and achieving continuous AI delivery without service interruptions. To be successful in this project, you should have: • Docker containerization experience • Cloud deployment fundamentals • Basic Kubernetes knowledge • ML/AI model deployment concepts

• Perform pre-deployment dependency checks
• Evaluate deployment decisions balancing performance, latency, and cost
• Implement zero-downtime release strategies
• Avoid costly technical debt from poor deployment choices

• Basic familiarity with relevant concepts
• Readiness for hands-on and case-based practice