| Instructor name | Instructor kaggle profile | Instructor GitHub profile |
|---|---|---|
| Himanshu Arora | kaggle | GitHub |
| Course Link | Language |
|---|---|
| End-to-End ML Project using MLOps | Hinglish |
Note: This is the free but quality course offered by CampusX.
Description:
This comprehensive course teaches you how to develop a machine learning project from start to finish utilizing MLOps practices. You will learn to streamline your ML workflow, enhance model deployment, and optimize your project for efficiency.
Key Highlights:
- Learn end-to-end ML project development
- Implement MLOps methodologies
- Optimize ML workflows
- Deploy and monitor ML models effectively
What you will learn:
- Develop end-to-end ML projects
- Understand the complete lifecycle of a machine learning project and learn to apply best practices for efficient development.
- Implement MLOps methodologies
- Discover how MLOps techniques can streamline your ML workflows, enhance collaboration, and improve model deploymen processes.
- Optimize model deployment -Learn strategies to deploy machine learning models effectively, monitor their performance, and ensure scalability and reliability.
| Instructor name | Link |
|---|---|
| Session 1 - Introduction to MLOPs and Version Control |  |
| Session 2 - Data and Model Versioning through DVC | |
| Session 3 - Data to Model Pipeline through DVC | |
| Session 4 - Data to Model Pipeline through DVC Contd | |