📅 2024-04-13 — Session: Implementing and Troubleshooting Model Pipelines
🕒 00:05–23:55
🏷️ Labels: Machine Learning, Model Pipeline, Api Integration, Data Preprocessing, Troubleshooting
📂 Project: Dev
⭐ Priority: MEDIUM
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.