MLOps Fundamentals
About
MLOps (Machine Learning Operations) applies DevOps principles to machine learning workflows. It ensures that ML models are deployed, monitored, and maintained efficiently in production environments. This module will provide hands-on experience in implementing MLOps principles using practical tools and techniques.
By the end of this module, you will be able to:
- Understand the key challenges of operationalizing ML models.
- Define MLOps and its core principles.
- Apply best practices for versioning, testing, and deploying ML models.
- Compare common MLOps tools and their use cases.
- Enhance an existing ML pipeline to incorporate essential MLOps components.
Content
| Lesson | Est. Time | Skills Focus |
|---|---|---|
| Setup | 2 min | Set up the development environment |
| Core Principles of MLOps | 25 min | Learn best practices and strategies |
| MLOps Tools & Technologies | 25 min | Explore MLflow and model tracking |
| Implementing MLOps | 25 min | Modify a prebuilt ML pipeline to follow MLOps principles |
| Total Content | ~90 min |