01Module 10 - AI Platform EngineeringBuild the internal platform that lets data scientists ship models to production in days, not months - covering MLOps architecture, experiment tracking, CI/CD for ML, and Kubernetes-native ML infrastructure.02MLOps Platform ArchitectureUnderstand the MLOps maturity model from Level 0 to Level 3, design the components of a complete ML platform, and build a realistic 12-month roadmap from ad-hoc to automated.03Ray CoreRay actors, tasks, object store, and building distributed Python applications.04Experiment TrackingDesign and govern ML experiment tracking at scale - from MLflow architecture to organizing 50 data scientists' experiments without chaos.05Ray ServeRay Serve for scalable ML model serving - deployments, pipelines, and autoscaling.06Model Registry and VersioningDesign a model registry that enables 3-minute rollbacks, full model lineage, and controlled staging-to-production promotion - turning model lifecycle management from a manual process into a reliable system.07vLLM ArchitecturePagedAttention, continuous batching, and vLLM's throughput optimisations.08CI/CD for MLBuild automated CI/CD pipelines for machine learning - from unit tests on transforms to canary deployments - so model degradation gets caught before it reaches users.09Triton Inference ServerNVIDIA Triton - model ensembles, dynamic batching, and multi-framework support.10Feature PlatformBuild a shared feature platform that eliminates cross-team feature duplication, ensures training-serving consistency, and serves fresh features at millisecond latency.11Kubeflow PipelinesKubeflow for ML workflow orchestration on Kubernetes.12Model Monitoring PlatformBuild production model monitoring infrastructure that catches data drift, prediction drift, and concept drift - detecting model degradation within 24 hours instead of two months.13Seldon and BentoMLSeldon Core and BentoML for model packaging and serving.14Kubernetes for MLUse Kubernetes as ML infrastructure - from GPU scheduling and device plugins to Kubeflow Pipelines and autoscaling - migrating ML workloads from VMs to K8s without disruption.15ML Platform ArchitectureDesigning an internal ML platform - abstractions, self-service, and developer experience.16Platform Engineering PatternsStandardising training, serving, and experimentation across a large ML team.17Self-Service ML PlatformBuild ML platforms that data scientists actually use - applying product thinking to internal tooling, from user research and notebook-to-production workflows to adoption metrics and guardrails.
