Lesson 7 : MLOps Foundation
Lesson 7 — Unified MLOps Control Plane
What We're Building Today
Today you build a unified MLOps control plane — a single platform that orchestrates the entire machine learning lifecycle: experiment tracking, data pipelines, distributed training, model serving, production monitoring, security governance, and cost optimization.
This is not a collection of disconnected notebooks. It is an integrated system with a shared API, centralized state, real-time observability, and a command-center dashboard — the kind of architecture platform teams deploy when ML stops being a research exercise and becomes a revenue-critical production capability.
Why It Matters
Most organizations fail at MLOps not because they lack tools, but because their tools are siloed:
Data engineers own pipelines in Airflow.
ML scientists own experiments in scattered notebooks.
Platform engineers own Kubernetes clusters nobody else understands.
FinOps owns a spreadsheet disconnected from GPU utilization.
Security owns a governance process that blocks releases at 4 PM on Fridays.
A unified control plane collapses these silos behind one API surface, one state model, and one operational vocabulary. Netflix's Metaflow, Uber's Michelangelo, and Spotify's ML infrastructure all converge on this pattern: abstract complexity, expose capabilities, reconcile state continuously.
Practical Outcome
By the end of this lesson you will have:
A FastAPI control plane exposing seven domain modules through REST and WebSocket APIs.
Real ML training with MLflow experiment tracking and scikit-learn model registration.
A live dashboard with module navigation, metrics, and activity streaming.
Docker-packaged deployment with health checks, Prometheus metrics, and persistent runtime volumes.
Integration tests validating the full pipeline from ingest to inference to drift detection.
High-Level Agenda
Understand the MLOps lifecycle as a distributed control plane problem
Design module boundaries: platform, pipeline, training, serving, monitoring, security, cost
Implement shared state reconciliation and event broadcasting
Wire real services: MLflow, sklearn, Prometheus, WebSocket feeds
Containerize API and UI with Docker Compose
Validate end-to-end with integration tests and production checklists
Extend the platform with advanced patterns: operators, GitOps, multi-cluster serving
Core Concepts: The MLOps Lifecycle
What is MLOps?
MLOps is the operational discipline of taking machine learning from experiment to production and keeping it there reliably. It applies the same engineering rigor that transformed software deployment — version control, CI/CD, observability, incident response — to the unique challenges of ML systems.
ML systems are not ordinary applications. They have three axes of change that traditional DevOps was never designed for:
Code — training scripts, feature logic, serving handlers
Data — distributions shift silently; yesterday's training set is today's liability
Models — artifacts with versioned weights, hyperparameters, and performance envelopes
When any axis changes independently, production behavior drifts. MLOps exists to make that drift visible, measurable, and recoverable.
Why This Matters
A model that scores 94% accuracy in a notebook is worthless in production if:
Nobody can reproduce the training run
The serving endpoint has no traffic splitting for safe rollout
Drift goes undetected for three weeks
GPU costs triple because nobody tracks spot utilization
A governance workflow was never approved before deployment
Production MLOps is not about tools. It is about contracts: between teams, between systems, and between desired state and actual state.
Real Production Context
At Uber, Michelangelo handles feature stores, training orchestration, and batch/online serving behind unified APIs. At Airbnb, ML platforms enforce that every model has lineage, monitoring, and rollback paths before receiving production traffic. At Spotify, squad-level autonomy is balanced against platform-wide standards for experiment tracking and model registry access.
The pattern is consistent: platform team builds the control plane; product teams consume capabilities through APIs and dashboards.
Core Concepts: Control Plane vs Data Plane
What is the Control Plane?
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