Enhanced Flask API with MLflow integration

• Day: 2024-04-19
• Time: 03:10 to 04:05
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Flask, Mlflow, API, Machine Learning, Python

Description

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.

Evidence

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• event_ids: []