AI Systems Design
Architect production AI at scale - data infrastructure, model serving, real-time systems, vector databases, GPU clusters, and the design patterns that hold up under real load.
10 Modules. Production to Platform.
From data infrastructure to GPU clusters - every layer of AI systems, explained with depth.
Systems Foundations
The 4-step design framework, requirements gathering, back-of-envelope estimation, latency vs throughput, CAP theorem for ML, and data system patterns.
What you'll master
- ML System Design Framework
- Requirements & Constraints
- Back-of-Envelope Estimation
- Latency vs Throughput Tradeoffs
- Consistency & Availability in ML
- Data Systems for ML
6 lessons
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Data Infrastructure
Data lakes, Spark batch processing, Kafka streaming, feature stores, data quality, Delta Lake versioning, and lakehouse architecture.
What you'll master
- Data Lakes & Data Warehouses
- Batch Processing with Spark
- Stream Processing with Kafka
- Feature Stores Architecture
- Data Quality & Validation
- Data Versioning with Delta Lake
- Lakehouse Architecture
7 lessons
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Model Serving
Serving architectures, batching strategies, quantization, TensorRT compilation, semantic caching, multi-model serving, inference scaling, and production monitoring.
What you'll master
- Serving Architectures (REST, gRPC, Triton)
- Static vs Dynamic vs Continuous Batching
- Model Quantization (INT8, GPTQ, AWQ)
- TensorRT & torch.compile Optimization
- Caching for ML (semantic, KV, prefix)
- Multi-Model Serving & A/B Testing
- Inference Scaling with KEDA
- Production Monitoring & Alerting
15 lessons
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Real-Time ML
Real-time inference design, online learning, streaming inference, low-latency optimization, event-driven ML, temporal features, and edge deployment.
What you'll master
- Real-Time Inference Design (1M QPS)
- Online Learning & Concept Drift
- Streaming Inference with Flink
- Low-Latency Optimization (NUMA, zero-copy)
- Event-Driven ML Architecture
- Temporal Feature Engineering
- Edge ML Deployment
11 lessons
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ML Architecture Patterns
Two-tower models, RAG system design, cascade ranking, multi-task learning, feedback loops, A/B testing, and reproducibility at scale.
What you'll master
- Two-Tower Models & ANN Search
- RAG System Design (hybrid search, reranking)
- Cascade & Funnel Architecture
- Multi-Task Learning Systems
- Feedback Loops & Data Flywheels
- Experimentation & A/B Testing
- Lambda & Kappa Architecture
12 lessons
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Industry Case Studies
End-to-end design walkthroughs: recommendation systems, search ranking, fraud detection, LLM infrastructure, computer vision, and ad click prediction.
What you'll master
- Recommendation Systems (YouTube-scale)
- Search & Retrieval Systems
- Fraud Detection (Stripe-scale)
- LLM-Powered Product Architecture
- Computer Vision at Scale
- Ad Click Prediction (CTR)
- Content Moderation Systems
12 lessons
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Vector Database Engineering
Vector similarity search, HNSW/IVF/PQ algorithms, Pinecone vs Qdrant vs pgvector, embedding pipelines, hybrid search, metadata filtering, and sharding.
What you'll master
- Vector Similarity Search Fundamentals
- ANN Algorithms (HNSW, IVF, PQ)
- Vector Databases Compared
- Embedding Generation Pipelines
- Hybrid Search (BM25 + Dense + RRF)
- Metadata Filtering & Multi-Tenancy
- Scalability & Sharding
- Production Vector DB Operations
8 lessons
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GPU / TPU Infrastructure
GPU architecture, CUDA memory management, distributed training (DDP, FSDP, ZeRO), training clusters, TPU pods, inference hardware, and GPU cost optimization.
What you'll master
- GPU Architecture & Roofline Model
- GPU Memory Management (ZeRO, gradient checkpointing)
- Distributed Training (DDP, FSDP, 3D parallelism)
- Training Infrastructure & Fault Tolerance
- TPU Architecture & JAX
- Inference Hardware (Inferentia, L4, Jetson)
- GPU Cost Optimization (MIG, spot)
7 lessons
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Cost & FinOps
ML cost models, training cost reduction, inference cost optimization, build vs buy analysis, cloud FinOps, efficiency economics, and ROI business cases.
What you'll master
- ML Cost Models & Unit Economics
- Training Cost Optimization
- Inference Cost Optimization
- Build vs Buy Analysis
- Cloud Cost Management (RI, Savings Plans)
- Model Efficiency Economics
- ML ROI & Business Cases
14 lessons
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AI Platform Engineering
MLOps maturity model, experiment tracking, model registry, CI/CD for ML, feature platforms, model monitoring, Kubernetes for ML, and self-service platforms.
What you'll master
- MLOps Platform Architecture (Level 0→3)
- Experiment Tracking at Scale
- Model Registry & Versioning
- CI/CD for ML Pipelines
- Feature Platform Design
- Model Monitoring & Drift Detection
- Kubernetes for ML Workloads
- Self-Service ML Platform UX
16 lessons
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Ready to design AI systems that scale?
From data infrastructure to real-time inference - the complete AI systems curriculum.
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