MLOPS · PRODUCTION

MLOps Production Guide

A workbook-style course for ML engineers who need to ship, operate, and scale ML systems in production. Covers problem framing, MLOps lifecycle, platform design, and the operational decisions that determine whether a model creates business value.

8 modules · 27 chapters

Scope

ML Engineer

Audience

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Curriculum

Foundations

The problem space and operating model for ML in production — framing, lifecycle, and platform decisions that determine whether any later modeling work matters.

→ML Problem Framing
→MLOps Blueprint & Operational Strategy
→MLOps Platforms

Engineering Foundation

The technical infrastructure and workflows that enable reproducible, scalable ML development — project structure, environments, and deployment automation.

→Project Planning & Tech Stack
→Environments, Branching & CI/CD Deployments

Data

Data pipeline architecture from sourcing through serving — discovery, cataloging, batch and streaming pipelines that feed feature engineering and model training.

→Data Sourcing, Discovery & Understanding
→Data Cataloging, Metadata & Lineage
→Data Engineering: Batch Pipelines
→Streaming Data Pipelines & Real-Time ML

Features & Model Development

The core ML loop — feature engineering, feature stores, model iteration, and experiment tracking that drives model performance and reproducibility.

→Feature Engineering in Production
→Feature Stores: Architecture, Implementation & Tradeoffs
→Model Development & Iteration
→Experiment Tracking & Hyperparameter Optimization
→Training Deep Learning Models: Production Playbook

Pipelines, Testing & Evaluation

ML pipeline orchestration, comprehensive testing strategies, and model evaluation frameworks that ensure models are production-ready before deployment.

→ML Training Pipelines & Orchestration
→Comprehensive ML Testing
→Model Evaluation, Validation & Registry

Deployment & Serving

Model packaging, inference optimization, and deployment strategies including progressive delivery and safe rollout patterns.

→Model Deployment Strategies & Packaging
→Inference Stack Optimization
→Progressive Delivery & Safe Rollout

Production Operations

Post-deployment lifecycle — monitoring, observability, drift detection, continual learning, and production experimentation at scale.

→Production Monitoring, Observability & Drift Detection
→Continual Learning & Model Retraining
→Production Experimentation & A/B Testing
→A/B Testing at Scale: Industry Lessons

Governance & Scale Mastery

Enterprise considerations — governance, compliance, responsible AI, and reference architectures for scaling ML systems across organizations.

→Model Governance, Compliance & Responsible AI
→Reference Architecture & Production Readiness
→LLMOps: MLOps for Large Language Models

Sample Slides