Enhanced Flask API with MLflow integration

📅 2024-04-19 — Session: Enhanced Flask API with MLflow integration

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

Session Goal

The primary goal of this session was to enhance a Flask API with improved machine learning model training, evaluation, and logging capabilities using MLflow.

Key Activities

• Developed a train_and_evaluate_model function for training a RandomForestRegressor model and integrating it into a Flask API endpoint.
• Updated the Flask endpoint to include MLflow logging, capturing parameters and metrics, and removing unnecessary profiling for cleaner code.
• Implemented methods to save model predictions and actual values as CSV files linked to MLflow experiment IDs, and generated diagnostic plots.
• Corrected the Flask route for MLflow integration to ensure proper JSON response handling.
• Revised the /predict endpoint to select the latest model and preprocessor files, with improved error handling.
• Reviewed best practices for Python module imports, emphasizing package structures and environment variable settings.

Achievements

• Successfully integrated MLflow logging into the Flask API, improving the tracking of model parameters and metrics.
• Enhanced the /predict endpoint for better model and preprocessor selection and error handling.
• Established a more maintainable project structure by adhering to Python import best practices.

Pending Tasks

• Further testing of the integrated Flask API with real-world data to ensure robustness and reliability.
• Documentation of the new API features and integration process for future reference.