ML Ops Course

Lesson 1 • What MLOps Solves

Defines the gap between experimental ML and production systems. Grounds the chapter by framing why operational discipline is essential for ML value delivery.

Lesson 2 • Core MLOps Principles

Introduces reproducibility, automation, and monitoring as foundational pillars. Connects each principle to measurable outcomes in model reliability.

Lesson 3 • DevOps Concepts Applied to ML

Translates CI/CD, infrastructure-as-code, and agile practices into ML contexts. Enables students to reuse DevOps tooling knowledge within ML pipelines.

Lesson 4 • Roles and Team Structures

Defines responsibilities of ML engineers, data scientists, and platform engineers. Clarifies collaboration boundaries that affect pipeline design decisions.

Lesson 1 • Feature Stores and Reuse

Explains offline and online feature stores and their role in consistency. Reduces training-serving skew by centralizing feature computation.

Lesson 2 • Data Lineage and Governance

Covers end-to-end lineage tracking and access control for ML datasets. Supports auditability and compliance requirements in regulated environments.

Lesson 3 • Data Validation and Quality

Introduces statistical and schema-based validation techniques applied at ingestion. Prevents silent data corruption from propagating into model training.

Lesson 4 • Dataset Versioning Strategies

Teaches immutable dataset snapshots, delta versioning, and metadata tagging. Enables full reproducibility of any historical training run.

Lesson 1 • Hyperparameter Management

Teaches systematic search strategies and configuration management for hyperparameters. Connects tuning discipline to faster convergence and fair model comparison.

Lesson 2 • Reproducibility Auditing

Defines reproducibility levels and methods for verifying experiment re-execution. Prepares students to diagnose and fix non-determinism in training pipelines.

Lesson 3 • Model Registry Basics

Introduces centralized model registries for storing and staging trained models. Provides the artifact management layer required for controlled deployment.

Lesson 4 • Experiment Tracking Fundamentals

Introduces logging of parameters, metrics, and artifacts during training runs. Establishes the habit of structured record-keeping central to reproducible ML.

Lesson 1 • Pipeline Design Principles

Covers modularity, idempotency, and failure isolation in pipeline architecture. Establishes design rules that make pipelines maintainable and debuggable.

Lesson 2 • Workflow Orchestration Tools

Surveys DAG-based orchestration frameworks and their scheduling capabilities. Enables students to select and configure orchestrators for diverse ML workloads.

Lesson 3 • Continuous Training Patterns

Covers scheduled retraining, event-driven retraining, and champion-challenger setups. Keeps models current with evolving data distributions automatically.

Lesson 4 • Automated Training Pipelines

Builds pipelines that automate data prep, training, and evaluation steps end-to-end. Reduces manual intervention and enables continuous training on new data.

Lesson 1 • Model Serving APIs and Contracts

Covers API schema design, versioning, and backward compatibility for model endpoints. Prevents breaking changes from disrupting downstream consumers.

Lesson 2 • Serving Infrastructure and Scaling

Introduces load balancing, auto-scaling, and resource allocation for inference. Ensures serving systems meet latency and throughput SLAs under variable load.

Lesson 3 • Containerization for Model Serving

Covers packaging models into containers with reproducible runtime environments. Enables portable, environment-agnostic model deployment across infrastructure.

Lesson 4 • Progressive Deployment Strategies

Teaches canary releases, blue-green deployments, and shadow mode testing. Reduces deployment risk by validating new models against live traffic safely.

Lesson 1 • Automated Model Validation Gates

Defines quality gates that block model promotion when evaluation criteria are unmet. Prevents underperforming models from reaching production automatically.

Lesson 2 • ML CI/CD Architecture

Maps CI/CD stages to ML-specific artifacts including data, code, and models. Provides the architectural blueprint for automated ML delivery pipelines.

Lesson 3 • Testing in the ML Delivery Pipeline

Integrates data validation, model tests, and serving tests into the CI/CD flow. Ensures every pipeline stage has automated quality verification before promotion.

Lesson 4 • Infrastructure Automation for ML

Applies infrastructure-as-code to provision training and serving environments consistently. Eliminates environment drift between development, staging, and production.

Lesson 1 • Alerting and Incident Response

Designs alert rules, escalation paths, and runbooks for ML production incidents. Reduces mean time to resolution through structured response procedures.

Lesson 2 • Observability Pillars for ML

Introduces logs, metrics, and traces as the three observability pillars applied to ML. Establishes the instrumentation foundation for all monitoring strategies.

Lesson 3 • Data Drift Detection

Covers statistical tests and distance metrics for detecting input distribution shifts. Enables early warning before model performance degrades in production.

Lesson 4 • Model Performance Monitoring

Tracks prediction quality metrics over time using ground truth and proxy signals. Connects performance trends to retraining and rollback decisions.

Lesson 1 • Cost Management for ML Infrastructure

Analyzes compute, storage, and serving costs and applies optimization strategies. Prevents runaway infrastructure spend as ML workloads scale across teams.

Lesson 2 • Model Governance and Auditability

Covers model cards, approval workflows, and audit trails for production models. Enables accountability and traceability required in regulated or high-stakes domains.

Lesson 3 • Platform Engineering for ML Teams

Designs internal ML platforms that abstract infrastructure complexity from practitioners. Accelerates team productivity through self-service tooling and golden paths.

Lesson 4 • MLOps Maturity Models

Introduces maturity frameworks from manual processes to fully automated ML platforms. Helps organizations assess current state and prioritize improvement investments.