📅 2024-04-15 — Session: Configured MLflow for Docker and Flask Integration
🕒 15:45–17:20
🏷️ Labels: Mlflow, Docker, Flask, Mlops, Experiment Tracking
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal:
The session aimed to integrate MLflow with Docker and Flask applications to enhance MLOps capabilities, focusing on experiment tracking, model versioning, and artifact management.
Key Activities:
- Explored MLOps tools like MLflow, DVC, and Kubeflow for managing model and data versioning.
- Created a workflow diagram using Graphviz to illustrate MLflow and DVC integration.
- Configured MLflow’s tracking URI for local and remote setups.
- Set up MLflow in a Docker container, modifying the Dockerfile and application settings for experiment tracking.
- Enhanced a Flask endpoint with MLflow for improved model retraining and experiment tracking.
- Resolved MLflow-related permission errors and tracking URI path issues.
- Implemented a naming convention for MLflow experiments to ensure meaningful and unique identifiers.
- Addressed file path discrepancies in Python for model saving/loading.
- Adjusted model loading in Flask applications to use standardized temporary directories.
- Updated model retrieval and dropdown selection in APIs to ensure accessibility of models.
Achievements:
- Successfully configured MLflow with Docker and Flask, resolving several configuration and permission issues.
- Developed a robust workflow for experiment tracking and model management in MLOps.
Pending Tasks:
- Further testing of the integrated system in a production-like environment to ensure stability and performance.
- Continuous monitoring and optimization of the MLflow setup as new requirements arise.