Uncover the transformative potential of MLOps in translating machine learning models from personal notebooks into operationalized models that drive tangible business outcomes.
Get Complete Hands-on Practical Learning Experience through Assignments & Projects for Proper Confidence Building
Get 1-1 Personal Chat Support for Doubt Clearance everyday between 6PM to 12Midnight (including weekends). Between 8PM to 9PM, the Teaching Assistants will be also available over Live Zoom Meeting for Doubt Clearance.
1. Key Ideas
2. Structure
3. Why its used
1. Streamlit Introduction
2. Installing Pycharm
3. Building a ML Application from scratch
4. Deploying ML Application using Streamlit and Github
1. Introduction to Flask and Fast API
2. Building a ML Application using Flask Framework
3. Deploying ML Model using API’s
1. Introduction to Docker
2. Building an Image using Docker
3. Containerization and Deployment
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