MLOps – 100+ Lab Exercises (Basic, Intermediate, Advanced)
🔰 Basic Level (30+ Exercises)
Goal: Establish core understanding of machine learning workflows, DevOps fundamentals, and cloud basics for MLOps.
Foundations of MLOps
Introduction to MLOps concepts and lifecycle.
Overview of ML model development workflow.
Understand version control (Git) for code and data.
Set up Python environments and package management.
Build and train simple ML models (scikit-learn, TensorFlow).
Data Engineering Basics
Data collection, cleaning, and preprocessing pipelines.
Work with data versioning tools (DVC, Delta Lake).
Automate data validation and monitoring.
Use Jupyter notebooks for experimentation.
Understand data storage and database options.
Infrastructure Setup
Introduction to cloud platforms (AWS, Azure, GCP).
Set up Kubernetes clusters for ML workloads.
Use Docker for containerizing ML models.
Deploy models locally using Flask or FastAPI.
Monitor compute resource usage and logs.
🚀 Intermediate Level (40+ Exercises)
Goal: Develop proficiency in automation, continuous integration/delivery (CI/CD), and scalable model deployment.
Model Deployment & Serving
Package ML models as REST APIs.
Use TensorFlow Serving and TorchServe.
Implement model versioning and rollback strategies.
Deploy ML models on Kubernetes with Kubeflow.
Set up serverless ML deployments (AWS Lambda, Azure Functions).
Automation & CI/CD Pipelines
Build automated ML pipelines using Jenkins or GitHub Actions.
Integrate testing frameworks for ML code and models.
Automate data and model validation.
Implement continuous training and retraining pipelines.
Use ML pipeline orchestration tools (Airflow, Prefect).
Monitoring & Observability
Monitor model performance and data drift.
Set up alerting and logging (Prometheus, Grafana).
Use explainability tools for model transparency.
Implement A/B testing and canary deployments.
Track metrics with MLflow or Weights & Biases.
🧠 Advanced Level (40+ Exercises)
Goal: Master enterprise-grade MLOps practices, governance, security, and innovation in AI lifecycle management.
Advanced Pipeline Engineering
Design scalable, distributed training pipelines.
Implement feature stores and metadata management.
Automate hyperparameter tuning and experimentation.
Use advanced orchestration with Kubeflow Pipelines.
Integrate with big data platforms (Spark, Hadoop).
Governance & Compliance
Implement data privacy and security best practices.
Manage model audit trails and reproducibility.
Ensure compliance with regulations (GDPR, HIPAA).
Apply role-based access controls and secrets management.
Conduct risk assessment and mitigation for ML models.
Innovation & Emerging Trends
Integrate edge AI and IoT with MLOps.
Use federated learning and privacy-preserving ML.
Explore AI model compression and optimization.
Implement reinforcement learning pipelines.
Leverage AI Ops for automated incident management.
Capstone Projects
Develop a fully automated end-to-end MLOps pipeline.
Deploy and monitor a real-time streaming ML model.
Implement scalable multi-model serving infrastructure.
Design a compliant ML governance framework.
Build an AI-driven monitoring dashboard with alerting.
✅ Tools & Technologies
Python, scikit-learn, TensorFlow, PyTorch
Docker, Kubernetes, Kubeflow, MLflow
Jenkins, GitHub Actions, Airflow, Prefect
Prometheus, Grafana, Weights & Biases
AWS SageMaker, Azure ML, GCP AI Platform