AI Architectures
Learn to design, build, and scale production AI systems. From neural network architectures to distributed training, model serving, and platform design — master the architecture patterns that power modern AI.
AI Architectures is the track on the model families themselves. Transformers, diffusion models, state-space models (Mamba), mixtures of experts, retrieval-augmented architectures, speculative decoding setups, multimodal fusion architectures, small language models, and the emerging alternatives are now enough of a zoo that an applied engineer benefits from a mental map of the families, not just a working familiarity with one or two.
We go deeper on the architectures that matter for production decisions: how attention actually works, why KV-cache sizing drives memory and latency, how MoE models change your serving and cost calculus, what state-space models offer that transformers do not, and how multimodal architectures compose. The lessons are math-aware but engineering-first: the mathematical details that affect deployment decisions get depth, while the theory that does not affect applied work gets a clear pointer to deeper material.
All Courses
20 comprehensive courses covering every aspect of AI system architecture.
Neural Network Architectures
AI Architecture Fundamentals
Master the core principles, components, and decision frameworks that underpin every successful AI system architecture.
7 LessonsTransformer Architecture Deep Dive
Understand the transformer architecture from attention mechanisms to modern variants and implementation details.
7 LessonsCNN Architectures
Explore the evolution of convolutional neural networks from LeNet to modern EfficientNet and beyond.
7 LessonsRNN & LSTM Architectures
Learn recurrent neural networks, LSTM, GRU, and sequence modeling architectures for temporal data.
7 LessonsGAN Architectures
Master generative adversarial networks from basic GANs to StyleGAN, CycleGAN, and training strategies.
7 LessonsDiffusion Model Architecture
Understand diffusion models including DDPM, DDIM, Stable Diffusion, and latent diffusion for generative AI.
7 LessonsAI Application Architectures
RAG Architecture Patterns
Design and build production retrieval-augmented generation systems with advanced retrieval and evaluation.
7 LessonsMulti-Agent System Architecture
Architect multi-agent AI systems with orchestration, tool use, memory, and scalable communication patterns.
7 LessonsMicroservices for AI
Decompose AI systems into microservices for model serving, feature computation, and scalable ML pipelines.
7 LessonsEvent-Driven AI Architecture
Build event-driven AI systems with Kafka, event sourcing, stream processing, and real-time inference.
7 LessonsDeployment & Infrastructure
Serverless AI Architecture
Deploy AI workloads on serverless platforms with Lambda, Azure Functions, and Cloud Functions patterns.
7 LessonsEdge AI Architecture
Design AI systems for edge deployment with model compression, quantization, and hybrid cloud-edge patterns.
7 LessonsReal-Time AI Architecture
Architect low-latency AI systems with streaming ML, online learning, feature stores, and caching strategies.
7 LessonsML Platform Architecture
Design internal ML platforms with experiment tracking, model registry, compute orchestration, and multi-tenancy.
7 LessonsData & Platform
Data Lake Architecture for AI
Build data lakes and lakehouses optimized for AI workloads with Delta Lake, data catalogs, and governance.
7 LessonsFeature Store Architecture
Design feature stores with online and offline serving, Feast, Tecton, and feature registry patterns.
7 LessonsModel Serving Architecture
Deploy models at scale with TensorFlow Serving, TorchServe, Triton, ensemble serving, and A/B testing.
7 LessonsAI Gateway Architecture
Build AI gateways with routing, rate limiting, model versioning, observability, and security layers.
7 LessonsTraining & Patterns
Distributed Training Architecture
Scale model training with data parallelism, model parallelism, pipeline parallelism, DeepSpeed, and FSDP.
7 LessonsAI Architecture Patterns & Anti-Patterns
Learn proven architecture patterns and common anti-patterns for data management, training, serving, and monitoring.
7 LessonsWhat You'll Learn
Skills you will gain across these 20 architecture courses.
Neural Network Design
Understand transformer, CNN, RNN, GAN, and diffusion model architectures at the implementation level.
System Architecture
Design production ML systems with microservices, event-driven patterns, and serverless architectures.
Deployment at Scale
Master model serving, distributed training, edge deployment, and real-time inference architectures.
Platform Engineering
Build ML platforms with feature stores, data lakes, model registries, and AI gateways.