ENGWK002 - Delivering the AI Stack

Course Description

Many organizations have moved beyond AI experimentation to integrating AI into mission-critical business operations. This shift creates new operational challenges: How do you ensure reliable AI service delivery? How do you track changes across complex AI systems? How do you maintain performance as AI models evolve?

This Workshop equips teams with practical frameworks for deploying and operating AI systems at enterprise scale. We'll use a "functional architecture" approach, providing you with a durable conceptual framework that shows how AI deployment components fit together, from data pipelines to model serving to monitoring systems. While specific tools evolve rapidly, this architectural understanding will help you evaluate new technologies and integrate emerging AI developments strategically.

We'll explore the fundamentals of MLOps and the machine learning life cycle, including common pain points and current tooling solutions. You'll gain hands-on experience with deployment strategies, containerization for reproducibility, and comprehensive monitoring approaches that ensure your AI systems deliver consistent business value.

Benefits to the Learner

• Build a comprehensive mental model of enterprise AI deployment architecture and infrastructure requirements
• Apply MLFlow to track critical parameters, metrics, and model performance across development and production environments
• Design reproducible AI pipelines with proper data versioning and change management protocols
• Execute blue/green deployment strategies to minimize risk when releasing AI model updates
• Implement monitoring systems to detect and respond to model drift and data quality issues
• Construct an end-to-end ML pipeline that meets enterprise reliability and compliance standards

Target Audience

• Technology managers and directors accountable for scaling fragmented AI pilots into standardized, enterprise-grade systems
• Technical team leads and principal engineers responsible for architecting reliable AI infrastructure and moving proofs of concept to production-ready applications
• Product managers and program directors who must ensure consistent AI performance across business units while communicating technical concepts to executive stakeholders
• Operations and compliance managers in regulated industries who require auditable, trackable AI processes to meet legal and reputational responsibilities
• IT directors and infrastructure leads tasked with building internal AI capabilities while managing vendor relationships and enterprise security requirements