MLOps: Production Machine Learning Pipelines

Course Overview #

This course covers the core concepts and real-world examples needed to operate machine learning models reliably in production environments.

Learning Objectives #

• Understand why MLOps is needed and how it differs from DevOps
• Build ML pipelines across data, training, serving, and monitoring
• Track experiments and manage model versions with MLflow
• Deploy and operate models on Kubernetes

Course Structure #

Part 1: From DevOps to MLOps #

• DevOps concepts and CI/CD pipelines
• Unique challenges of ML projects
• Why MLOps matters and its key principles

Part 2: Real-world ML system issues #

• Model drift and data changes
• Hidden feedback loops and reproducibility issues
• Ensuring environment consistency

Part 3: Components of an ML pipeline #

• Data Pipeline: data collection, validation, preprocessing, splitting
• Training Pipeline: model training, hyperparameter tuning, evaluation, validation
• Serving Pipeline: model registry, containerization, deployment, API serving
• Monitoring Pipeline: performance monitoring and alerts

Part 4: Experiment and model version management with MLflow #

• Experiment tracking with MLflow Tracking
• Model versioning with MLflow Model Registry
• Managing model lifecycle stages (Staging, Production, Archived)
• Comparing experiments and selecting the best model via UI

Part 5: Model deployment strategies #

• Batch prediction vs real-time prediction
• Rolling Update, Blue-Green, Canary, A/B Testing
• Pros, cons, and application scenarios for each strategy

Part 6: Scalable systems with Kubernetes #

• Core concepts of Kubernetes
• Building lightweight clusters with k3s
• Container orchestration and resource management

Teaching Method #

• Online/Offline: Zoom or in-person sessions
• Hands-on focused: Step-by-step labs using the ops_demo project
• 1:1 Feedback: Reviews of your pipeline designs and model operations

Target Audience #

• Developers who want to deploy and operate ML models in production
• ML engineers productionizing data scientists’ experiments
• Anyone interested in stable ML system operation and automation

Prerequisites #

• Basic Python and machine learning knowledge
• Docker fundamentals (containers, images)
• Basic Linux/terminal experience

Main Hands-on Project #

ops_demo: https://github.com/CodeCompose7/ops_demo

• 0.0.1: Base Python project structure
• 0.0.2: ML pipeline implementation
• 0.0.3: MLflow integration
• 0.0.4: Kubernetes deployment

MLOps vs DevOps Key Differences #

Core Principles of MLOps #

1. Automation: Automate data processing, model training, deployment, and retraining.
2. Reproducibility: Same data + same code = same result.
3. Monitoring: Track model performance, data quality, and anomalies in real time.
4. Collaboration: Enable collaboration between data scientists and engineers, sharing experiments and managing model versions.

Tool Stack #

• Experiment Tracking: MLflow, Weights & Biases
• Pipelines: Kubeflow, Apache Airflow
• Data Management: DVC, Feast
• Monitoring: Evidently, WhyLabs
• Cloud: AWS SageMaker, Google Vertex AI

Contact #

For course schedule and pricing inquiries, please reach out via email.