MLOps & Model Deployment
Production ML engineering — experiment tracking, model registries, CI/CD pipelines, and serving infrastructure.
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Course Content
Foundations
Core concepts and groundwork
Introduction to MLOps: Principles and the ML Lifecycle
Reading
Experiment Tracking with MLflow
Reading
Data Versioning with DVC
Reading
Feature Stores: Feast and Tecton
Reading
Foundation Practice: ML Experiment Pipeline
Exercise
Model Training Pipelines with Kubeflow Pipelines
Reading
Core Skills
Essential techniques and patterns
Orchestrating ML Workflows with Airflow and Prefect
Reading
Hyperparameter Optimisation with Optuna
Reading
Distributed Training: Horovod and PyTorch DDP
Reading
Training Pipeline Practice: AutoML with Optuna
Exercise
Model Packaging: Docker, ONNX, and Serialisation
Reading
Model Registry: MLflow, Vertex AI, and SageMaker
Reading
Applied Practice
Hands-on, real-world scenarios
REST API Serving with FastAPI and TorchServe
Reading
Batch Inference vs Online Inference
Reading
Serving Practice: Model API with Autoscaling
Exercise
Model Monitoring: Data Drift and Concept Drift
Reading
Evidently AI: Monitoring Reports and Test Suites
Reading
A/B Testing and Shadow Deployment
Reading
Advanced Topics
Deeper, more complex material
Feedback Loops and Continuous Training
Reading
Mid-Course Project: End-to-End MLOps Pipeline
Project
CI/CD for ML: GitHub Actions and DVC Pipelines
Reading
Infrastructure as Code for ML: Terraform + SageMaker
Reading
Kubernetes for ML: KServe and Seldon Core
Reading
GPU Management and Resource Scheduling
Reading
Production & Scale
Building for the real world
CI/CD and Infrastructure Practice
Exercise
Model Explainability in Production: SHAP and LIME
Reading
Fairness Monitoring and Bias Detection
Reading
Cost Optimisation: Spot Instances and Model Compression
Reading
Security: Model Theft and Adversarial Attacks
Reading
Governance and Security Practice
Exercise
Mastery & Capstone
Projects and final review
LLMOps: Fine-Tuning, Evaluation, and Deployment at Scale
Reading
Vector Database Management and Embedding Pipelines
Reading
Observability: Prometheus, Grafana, and ML Metrics
Reading
Building an Internal ML Platform
Reading
Capstone: Production MLOps Platform
Project
100% Free · All lessons unlocked
Topic Overview
What you'll learn
- Core concepts and fundamentals of MLOps & Model Deployment
- Industry best practices and design patterns
- Hands-on exercises with real-world scenarios
- Performance optimization and advanced techniques
- Building production-ready applications