01Module 05: ML Architecture PatternsA deep dive into the architectural patterns that power production ML systems - from Lambda/Kappa to multi-tenant platforms.02Lambda and Kappa Architecture for ML SystemsMaster Lambda and Kappa architecture - the two dominant patterns for building ML systems that handle both historical and real-time data at scale.03Two-Tower ModelsHow dual encoder architectures power billion-scale recommendation and search by separating user and item representations and querying them with approximate nearest neighbor search.04Microservices for ML SystemsLearn when and how to decompose ML systems into microservices - covering feature services, model services, service mesh, gRPC, and circuit breakers.05RAG System DesignHow to design Retrieval Augmented Generation systems for production - from naive RAG to advanced pipelines with chunking strategies, hybrid search, reranking, and RAG evaluation.06Cascade and Funnel ArchitectureHow multi-stage ranking systems reduce millions of candidates to a final ranked list within strict latency budgets - the architecture behind every major search and recommendation system.07Event Sourcing for ML SystemsLearn how event sourcing enables auditable, reproducible ML systems - covering the event log, Kafka as an event store, temporal queries, and the projection pattern.08ML Platform DesignLearn how to design internal ML platforms that enable data scientists and engineers to train, deploy, and monitor models efficiently - covering platform components, build vs buy, and real-world case studies.09Multi-Task Learning SystemsHow production ML systems share representations across multiple objectives simultaneously - covering hard vs soft parameter sharing, loss balancing, gradient conflicts, and negative transfer detection.10Feedback Loops and Data FlywheelsHow recommendation systems create self-reinforcing feedback loops, how to detect them, and how inverse propensity weighting and exploration strategies break them to enable unbiased learning.11Reproducibility and Auditability in ML SystemsLearn how to build fully reproducible ML systems - covering the reproducibility stack, DVC, MLflow, Docker, seed management, GDPR compliance, and financial model audits.12Experimentation and A/B Testing for ML SystemsHow to design statistically rigorous experiments for ML systems - Bayesian vs frequentist A/B tests, network interference, interleaving, switchback experiments, and guardrail metrics.13Multi-Tenant ML PlatformsLearn how to design ML platforms that safely serve multiple teams from shared GPU infrastructure - covering Kubernetes isolation, fair scheduling, data isolation, cost attribution, and quota management.
