Enhanced ML model integration with Flask and MLflow

📅 2024-04-19 — Session: Enhanced ML model integration with Flask and MLflow

🕒 03:10–04:05
🏷️ Labels: Flask, Mlflow, Randomforestregressor, API, Machine Learning
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary objective was to enhance the integration of machine learning models with a Flask API, incorporating MLflow for logging and tracking.

Key Activities

• Developed a train_and_evaluate_model function to encapsulate training of a RandomForestRegressor and integrated it into a Flask API endpoint.
• Updated the Flask endpoint to include MLflow logging for model retraining, ensuring parameters and metrics are logged effectively.
• Implemented functionality to save model predictions and actual values as CSV files in MLflow, along with generating diagnostic plots.
• Corrected a Flask route to ensure proper integration with MLflow, fixing the return statement alignment.
• Revised the /predict endpoint to select the latest model and preprocessor, enhancing error handling and managing missing values.
• Reviewed best practices for Python module imports to improve project structure and maintainability.

Achievements

• Successfully integrated MLflow logging into the Flask API, improving model tracking and experiment management.
• Enhanced the /predict endpoint for better model and preprocessor selection, ensuring robust error handling.

Pending Tasks

• Further testing is required to validate the robustness of the updated endpoints and logging mechanisms.
• Consider implementing additional error logging and monitoring for the Flask application.