📅 2024-04-07 — Session: Developed MLOps strategy for diamond pricing app
🕒 15:50–16:35
🏷️ Labels: Mlops, Machine Learning, Project Management, Diamond Pricing
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The goal of this session was to develop a comprehensive MLOps strategy for a diamond price prediction application, focusing on enhancing the Flask application, optimizing Docker usage, planning cloud infrastructure, and establishing documentation best practices.
Key Activities
- Created a detailed agenda outlining objectives related to MLOps, Flask, Docker, and cloud technologies.
- Developed guiding questions to steer the project discussion, covering objectives, data quality, model metrics, infrastructure, user needs, compliance, budget, and timeline.
- Provided a comprehensive project overview, detailing objectives, current status, data sources, model performance, cloud preferences, infrastructure, use cases, security, budget, and timeline.
- Outlined a comprehensive MLOps strategy, detailing the lifecycle from model development to deployment, monitoring, and re-training.
- Structured a 10-day MLOps workflow plan focusing on quick setup, model training, deployment, and documentation.
- Planned integration of a new ‘special’ characteristic into the diamond dataset to enhance UI and model training for unique diamond pricing.
Achievements
- Successfully outlined a comprehensive MLOps strategy and a structured 10-day workflow plan.
- Developed a clear understanding of the project’s objectives, current status, and future direction.
Pending Tasks
- Implement the 10-day MLOps workflow plan.
- Integrate the ‘special’ characteristic into the diamond dataset and adjust the model training accordingly.