Master AI Systems Design
A production-grade curriculum for engineers who design ML systems that scale.
Most ML courses end at model.predict().
This curriculum starts there - and teaches you how to serve, scale, monitor, and pay for ML systems in production.
The Curriculum
10 modules. From systems foundations to platform engineering - the complete ML systems stack.
Systems Foundations
Who it's for: Engineers learning to think in systems, not just models.
| Module | Topics |
|---|---|
| 01 - Systems Design Foundations | ML system design framework, requirements, architecture patterns, trade-offs |
| 02 - Data Infrastructure | Data lake and warehouse design, ingestion patterns, storage for ML |
| 03 - Model Serving | REST vs. gRPC, batching strategies, model servers, canary deployments, scaling |
Real-Time and Architecture
Who it's for: Engineers building low-latency ML systems and choosing architecture patterns.
| Module | Topics |
|---|---|
| 04 - Real-Time ML Systems | Real-time inference design, streaming pipelines, feature freshness, latency budgets |
| 05 - ML Architecture Patterns | Lambda and Kappa architecture, microservices for ML, event-driven ML, edge deployment |
| 06 - Case Studies | Recommendation systems, search ranking, fraud detection, content moderation at scale |
Infrastructure
Who it's for: Engineers working with vector databases, GPUs, and ML infrastructure.
| Module | Topics |
|---|---|
| 07 - Vector Database Engineering | Vector search fundamentals, HNSW, IVF, quantization, Pinecone, Weaviate, Milvus |
| 08 - GPU and TPU Infrastructure | GPU architecture for ML, multi-GPU training, memory optimization, TPU programming |
| 09 - Cost and FinOps for AI | AI cost drivers, spot instances, right-sizing, inference cost optimization, budgeting |
Platform Engineering
Who it's for: Engineers building internal ML platforms and tooling.
| Module | Topics |
|---|---|
| 10 - AI Platform Engineering | MLOps platform architecture, self-serve ML, developer experience, governance |
What You Will Be Able to Do
After completing this curriculum:
- Design end-to-end ML systems from data ingestion to model serving and monitoring
- Architect real-time ML pipelines with sub-100ms latency budgets
- Choose and operate vector databases for similarity search at scale
- Optimize GPU utilization and manage ML infrastructure costs
- Build internal ML platforms that make your team 10x more productive
- Ace ML system design interviews at top tech companies
The Engineering Standard
Every lesson in this curriculum:
- Opens with a real system design scenario or production incident
- Covers architecture diagrams, trade-offs, and capacity planning
- Includes working implementations with production-grade patterns
- Closes with system design interview questions at senior+ level
This is not a tutorial platform. It is an engineering curriculum.
Career Outcomes
Prepared for roles including:
- ML Systems Engineer
- AI Infrastructure Engineer
- ML Platform Engineer
- AI Systems Architect
- Senior ML Engineer (Systems)
Certification (Coming Soon)
EngineersOfAI - AI Systems Design Certification
Practical. Architecture-focused. Interview-ready. For engineers who design ML systems that work at scale - not just on a notebook.