Optimized Performance for Xtream AI Diamond Valuation

📅 2024-04-18 — Session: Optimized Performance for Xtream AI Diamond Valuation

🕒 17:55–20:15
🏷️ Labels: Performance, Optimization, Flask, Mlflow, API, Benchmarking
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session focused on enhancing the performance and optimization of the Xtream AI Diamond Valuation System, including benchmarking, code optimization, and performance monitoring.

Key Activities

• Developed a comprehensive checklist for performance optimization, covering areas like benchmarking and load handling.
• Conducted performance review and benchmarking to identify KPIs and establish baseline measurements.
• Set up and monitored KPIs such as response time and CPU usage for the valuation system.
• Integrated performance monitoring features into the application UI using Flask and JavaScript.
• Implemented API performance testing and troubleshooting for Flask endpoints.
• Profiled the /retrain endpoint using cProfile and line_profiler to optimize model retraining.
• Analyzed and optimized MLflow model logging performance.

Achievements

• Successfully integrated API performance testing into the UI and resolved 404 errors in Flask.
• Optimized the MLflow logging process, reducing execution time significantly.

Pending Tasks

• Further optimization of MLflow’s log_model function, focusing on file operations and server interactions.
• Continued analysis of profiling data to identify additional performance bottlenecks.