Prerequisites
- Basic understanding of Machine Learning concepts
- Familiarity with Python programming
- Basic knowledge of Git (recommended but not mandatory)
- Understanding of how ML models are trained (helpful)
- Interest in deploying ML models to production
What Will You Learn
- What MLOps is and why it is important
- The end-to-end Machine Learning lifecycle
- Key challenges in deploying ML models to production
- Core components of MLOps (versioning, CI/CD, monitoring)
- High-level MLOps architecture and workflows
- How modern companies manage production ML systems
- Roadmap to becoming an MLOps Engineer
