AI System Architect Professional Certificate

Design AI systems that scale and stay reliable: system design, distributed systems, ML and generative AI patterns, and platforms.

Learn system design and the foundations of designing AI systems, then the distributed systems ideas they rest on. Study proven design patterns for machine learning and for generative AI, how enterprises build data and ML platforms, and how to run and serve large language models on Kubernetes quickly and at a sensible cost. Every course ends with a design project your instructors review.

Prepares you for

AI architect, Solutions architect, ML platform engineer, Staff engineer

8 courses in this Professional Certificate

  • System Design Fundamentals: Scale, Caching and Load Balancing: Design systems that scale: estimate the load, then choose load balancers, caches, databases and queues, and defend every trade-off. (Intermediate, about 17 hours)
  • Foundations of AI System Design: Scope a machine learning system and design its data, features, models, deployment and monitoring, with a design document that catches data leakage before it ships. (Intermediate, about 21 hours)
  • Distributed Systems: Replication, Consensus and Consistency: Reason precisely about systems that span many machines: clocks, replication, consistency models, Raft consensus and distributed transactions, with simulations you build and break. (Advanced, about 19 hours)
  • Machine Learning Design Patterns: Solve the recurring problems of ML systems with proven patterns for representing data, framing problems, training, serving and reproducibility, and know what each one costs. (Intermediate, about 25 hours)
  • Generative AI Design Patterns: Build robust GenAI applications from proven patterns: structured output, RAG, reasoning, agents with tools and hand-offs, guardrails and evaluation, each built, measured and its trade-offs known. (Advanced, about 29 hours)
  • Enterprise Data and ML Platforms: Architect data lakes, lakehouses, streaming pipelines, hybrid cloud deployments and governed MLOps for an enterprise, and build working miniatures of each on your laptop. (Advanced, about 26 hours)
  • Operationalising LLMs on Kubernetes: Deploy, scale, observe and upgrade open large language models on Kubernetes: inference servers, model storage, GPU scheduling, queue-based autoscaling and safe rollouts. (Advanced, about 24 hours)
  • Efficient Inference: Quantisation, Distillation and Batching: Make language models faster, smaller and cheaper to serve: find where time and memory go, batch continuously, quantise weights and activations with the maths behind it, and distil, prune and speculate. (Advanced, about 18 hours)