Implementing and Troubleshooting Model Pipelines
- Day: 2024-04-13
- Time: 00:05 to 23:55
- Project: Dev
- Workspace: WP 2: Operational
- Status: In Progress
- Priority: MEDIUM
- Assignee: Matías Nehuen Iglesias
- Tags: Machine Learning, Model Pipeline, Api Integration, Data Preprocessing, Troubleshooting
Description
Session Goal
The session aimed to enhance and troubleshoot various aspects of machine learning model pipelines, including naming conventions, training, evaluation, retraining, and data preprocessing.
Key Activities
- Discussed best practices for naming conventions in model saving to ensure clarity and organization.
- Outlined steps to fit a machine learning model using a pipeline, evaluate its performance, and save the trained model with a descriptive filename.
- Developed methods for saving model predictions to CSV files during retraining to enhance data accessibility.
- Implemented dynamic model retraining and plot updating in web applications using JavaScript and Flask.
- Troubleshot issues related to undefined model names in API calls and float conversion errors in model predictions.
- Integrated preprocessing steps in model prediction pipelines and addressed errors related to unknown categories in data transformation.
Achievements
- Established a comprehensive guide for naming conventions and model management.
- Successfully implemented and tested model training, evaluation, and retraining pipelines.
- Enhanced API endpoints for better error handling and data preprocessing integration.
Pending Tasks
- Further refine the preprocessing pipeline to handle new categories more gracefully.
- Continue testing and debugging API endpoints to ensure robust backend communication and frontend updates.
Evidence
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- event_ids: []