Introduction
Designing AI systems requires integrating algorithms, data pipelines, hardware, and monitoring tools into cohesive architectures. This course provides a full view of how production AI systems are architected. Participants will learn best practices for scalability, reliability, and performance. Real-world architectures from major companies help illustrate key concepts. By the end, learners will understand how to design, evaluate, and improve AI systems holistically.
Course Objectives
- Learn principles of AI system architecture
- Understand data pipelines and storage systems
- Design scalable AI model serving solutions
- Explore monitoring, logging, and observability
- Implement best practices for production AI
Target Audience
- ML engineers
- Software architects
- DevOps and MLOps engineers
- Technical product leads
- Advanced AI engineering students
Course Outline
- 5 Sections
- 0 Lessons
- 5 Days
Expand all sectionsCollapse all sections
- Day 1: AI System Foundations• AI project lifecycle
• System components
• Architecture patterns
• Cloud vs. on-prem systems
• Case studies0 - Day 2: Data Pipelines & Storage• ETL/ELT pipelines
• Streaming vs. batch
• Feature stores
• Data governance
• Hands-on: Design a pipeline0 - Day 3: Model Serving & APIs• Real-time inference
• Scaling model servers
• Load balancing
• Latency optimization
• Hands-on: Build a serving API0 - Day 4: MLOps & Lifecycle Management• CI/CD for ML
• Model versioning
• Experiment tracking
• Monitoring and alerts
• Hands-on: MLOps pipeline0 - Day 5: System Optimization & Review• Cost optimization
• Reliability engineering
• Security considerations
• Architecture review
• Capstone presentation0
Instructor
Lessons:
