Course Overview
The MLOps (Machine Learning Operations) course focuses on operationalizing machine learning models in production environments. It bridges the gap between Data Science, DevOps, and Cloud Engineering by teaching how to automate model training, deployment, monitoring, and lifecycle management.
Learners will gain hands-on experience with model versioning, CI/CD for ML pipelines, containerization, Kubernetes, monitoring, and cloud-based ML platforms. The course prepares professionals to build scalable, reliable, and production-ready ML systems.
Course Content
Module 1: Introduction to MLOps (4 Hours)
What is MLOps?
ML lifecycle vs DevOps lifecycle
Challenges in deploying ML models
MLOps architecture overview
Module 2: ML Pipeline Fundamentals (6 Hours)
Data ingestion and preprocessing pipelines
Model training workflows
Experiment tracking concepts
Reproducibility in ML
Module 3: Version Control & Collaboration (6 Hours)
Git for ML projects
Data and model versioning
MLflow basics
DVC introduction
Module 4: CI/CD for Machine Learning (8 Hours)
CI/CD concepts for ML
Automating model training
Model validation & testing
Pipeline orchestration
Module 5: Containerization & Deployment (8 Hours)
Docker for ML applications
API development for model serving
Deployment strategies
Introduction to Kubernetes
Module 6: Cloud-Based MLOps (8 Hours)
AWS SageMaker / Azure ML overview
Managed ML services
Cloud deployment pipelines
Storage and compute optimization
Module 7: Monitoring & Model Management (8 Hours)
Model performance monitoring
Drift detection (data & concept drift)
Logging and observability
Retraining strategies
Module 8: Security & Governance (6 Hours)
Secure ML pipelines
Data privacy and compliance
Access control
Responsible AI basics
Module 9: Capstone Project (6 Hours)
End-to-end ML pipeline
Model training → deployment → monitoring
CI/CD integration
Presentation & review
Career Opportunities
– MLOps Engineer
– ML Engineer
– AI Platform Engineer
– Cloud AI Engineer
– DevOps for AI Specialist
Course Outcomes
After completing this course, learners will be able to:
– Understand the MLOps lifecycle and architecture
– Build reproducible ML pipelines
– Implement CI/CD for ML models
– Containerize and deploy ML applications
– Monitor model performance and detect drift
– Manage model versions and experiments
– Deploy ML systems on cloud platforms
– Apply governance and security best practices
– Design scalable, production-ready ML solutions
