MLOps Discipline

Discipline Track | 6 Modules | ~4 hours total

Overview

MLOps brings engineering rigor to machine learning. Most ML projects fail not because of bad models, but because teams can’t operationalize them. Data scientists build prototypes; MLOps turns them into production systems.

This track covers the complete ML lifecycle—from experiment tracking and feature stores to model serving, monitoring, and automated pipelines—giving you the skills to deploy and maintain ML systems at scale.

Prerequisites

Before starting this track:

• Observability Theory Track — Monitoring fundamentals
• Basic machine learning concepts (training, inference, models)
• Python programming experience
• Understanding of CI/CD concepts
• Kubernetes basics (helpful but not required)

Modules

#	Module	Complexity	Time
5.1	MLOps Fundamentals	[MEDIUM]	35-40 min
5.2	Feature Engineering & Stores	[COMPLEX]	40-45 min
5.3	Model Training & Experimentation	[COMPLEX]	40-45 min
5.4	Model Serving & Inference	[COMPLEX]	40-45 min
5.5	Model Monitoring & Observability	[COMPLEX]	40-45 min
5.6	ML Pipelines & Automation	[COMPLEX]	40-45 min

Learning Outcomes

After completing this track, you will be able to:

1. Understand MLOps maturity — From notebooks to automated pipelines
2. Build feature stores — Ensure consistency between training and serving
3. Track experiments — Reproduce results, compare approaches systematically
4. Deploy models — KServe, canary deployments, A/B testing
5. Monitor ML systems — Detect drift, track performance without labels
6. Automate pipelines — Kubeflow, continuous training, CI/CD for ML

Key Concepts

The ML Lifecycle

┌─────────────────────────────────────────────────────────────────┐
│                      ML LIFECYCLE                                │
│                                                                  │
│  DATA                 EXPERIMENTATION           PRODUCTION       │
│  ┌──────────┐        ┌──────────┐            ┌──────────┐       │
│  │  Data    │        │  Model   │            │  Model   │       │
│  │ Ingestion│───────▶│ Training │────────────▶│ Serving  │       │
│  └────┬─────┘        └────┬─────┘            └────┬─────┘       │
│       │                   │                       │              │
│  ┌────▼─────┐        ┌────▼─────┐            ┌────▼─────┐       │
│  │  Data    │        │  Model   │            │  Model   │       │
│  │Validation│        │Validation│            │Monitoring│       │
│  └────┬─────┘        └────┬─────┘            └────┬─────┘       │
│       │                   │                       │              │
│  ┌────▼─────┐        ┌────▼─────┐            ┌────▼─────┐       │
│  │ Feature  │        │  Model   │            │ Trigger  │       │
│  │  Store   │        │ Registry │            │ Retrain  │◀──────┘
│  └──────────┘        └──────────┘            └──────────┘       │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Core Principles

1. Reproducibility — Every training run must be reproducible
2. Automation — Automate everything from training to deployment
3. Versioning — Version code, data, AND models
4. Monitoring — ML systems fail silently; monitor everything
5. Continuous Training — Models degrade; keep them fresh

MLOps vs DevOps

Aspect	DevOps	MLOps
Artifact	Code	Code + Data + Model
Testing	Unit, integration	+ Model validation, drift tests
Versioning	Git	Git + DVC/MLflow
Monitoring	Infrastructure	+ Data quality, model performance
CI/CD	Build, test, deploy	+ Train, validate, serve

Tools Covered

Category	Tools
Experiment Tracking	MLflow, Weights & Biases, Neptune
Feature Stores	Feast, Tecton, Hopsworks
Model Serving	KServe, Seldon Core, BentoML, TorchServe
Pipeline Orchestration	Kubeflow Pipelines, Apache Airflow, Argo
Monitoring	Evidently, WhyLabs, Arize, NannyML
Hyperparameter Tuning	Optuna, Katib, Ray Tune
Platforms	Kubeflow, SageMaker, Vertex AI, Databricks

Study Path

Module 5.1: MLOps Fundamentals
     │
     │  Why ML is different, maturity levels
     ▼
Module 5.2: Feature Engineering & Stores
     │
     │  Training/serving skew, Feast
     ▼
Module 5.3: Model Training & Experimentation
     │
     │  MLflow, HPO, reproducibility
     ▼
Module 5.4: Model Serving & Inference
     │
     │  KServe, deployment patterns
     ▼
Module 5.5: Model Monitoring & Observability
     │
     │  Drift detection, Evidently
     ▼
Module 5.6: ML Pipelines & Automation
     │
     │  Kubeflow, CI/CD for ML
     ▼
[Track Complete] → ML Platforms Toolkit

Related Tracks

• Before: Observability Theory — Monitoring foundations
• Related: IaC Discipline — Infrastructure provisioning for ML platforms
• Related: DevSecOps — Security for ML pipelines
• After: ML Platforms Toolkit — Hands-on implementations
• After: IaC Tools Toolkit — Terraform modules for ML infrastructure

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“A model is only as good as the system that serves it. MLOps is that system.”