Understanding MLops in the Context of Movie Website

I. Introduction to MLops (Machine Learning Operations)

A. Key Aspects

• Complexity
• Error Handling
• Handling New Data
• Deterministic Models
• Zero Drift

B. Context: Movie Website Architecture

• Components:

• Database (SQL)
• Backend
• Frontend
• Recommender System (Model)

• Workflow:

• Database → Backend → Frontend
• Model: Retrieves data, calculates ratings, selects top movies
• Frontend displays recommendations to users

• Deterministic and Zero Drift:

• Changes in the database (e.g., new movies) don't affect other parts due to the deterministic nature of the model.
• White box model: Explains why specific recommendations are made.

II. Introduction of Machine Learning

A. Complexity Increase

• User Engagement Issue:

• Loss of customers, decreased engagement
• Move from recommending only top movies to personalized recommendations.

• Personalized Recommendation:

• Consider user history or collaborative recommender system.
• Example: User A and B have similar preferences; recommend a movie liked by B to A.

• Technology Stack Changes:

• Backend: Node, JS, or Python files
• Frontend: HTML, CSS, JS
• Emphasis on collecting quality data for personalized recommendations.

B. Introduction of Data Warehouse

• Data Conscious Approach:

• Collecting user data on preferences and interactions.
• Introduction of Data Warehouse for handling data separately from the main database.

• Data Warehouse Importance:

• Building models separate from the main database.
• Integration of external data (e.g., IMDb ratings) for model enhancement.

• ETL Process:

• Extraction, Transformation, and Load
• API used to transfer data to the Data Warehouse.

C. Collaboration and Complexity

• Increasing Complexity:

• More team collaboration.
• Involvement of data science teams in model building.

III. Error Handling and Drift Management

A. Error Debugging

• Error Sources:

• Data, Code, Model
• Debugging becomes critical in a more complex system.

B. Handling New Data

• Data Engineering Team:

• Use ETL to bring new data into the Data Warehouse.
• Data Science Team: Retraining the model based on the updated data.

C. Probabilistic Nature and Governance

• Privacy Issues:

• Model may recommend content inappropriate for certain audiences.
• Necessity for updating privacy policies.

D. Drift in Data

• User Preferences Change:

• Personal events (e.g., breakup) can shift preferences.
• External factors (e.g., celebrity endorsement) influence movie popularity.

IV. Multi-Model Approach

• Complexity Escalation:

• Introduction of multiple models for different purposes (e.g., recommendations, relevant ads).

V. Conclusion

• Summary:

• MLops introduces complexity in the form of personalized recommendations, data warehousing, collaboration, error handling, and drift management.
• Requires a multi-disciplinary approach involving data engineering, data science, and continuous adaptation.