Event Sourcing for ML Systems
Learn how event sourcing enables auditable, reproducible ML systems - covering the event log, Kafka as an event store, temporal queries, and the projection pattern.
7 docs tagged with "architecture-patterns"
View all tagsLambda and Kappa Architecture for ML Systems
Master Lambda and Kappa architecture - the two dominant patterns for building ML systems that handle both historical and real-time data at scale.
Microservices for ML Systems
Learn when and how to decompose ML systems into microservices - covering feature services, model services, service mesh, gRPC, and circuit breakers.
ML Platform Design
Learn 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.
Module 05: ML Architecture Patterns
A deep dive into the architectural patterns that power production ML systems - from Lambda/Kappa to multi-tenant platforms.
Multi-Tenant ML Platforms
Learn 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.
Reproducibility and Auditability in ML Systems
Learn how to build fully reproducible ML systems - covering the reproducibility stack, DVC, MLflow, Docker, seed management, GDPR compliance, and financial model audits.