galafis/ibm-machine-learning-capstone
Machine Learning Engineering Professional Certificate Capstone Project - Enterprise MLOps platform with automated pipelines
What's novel
Machine Learning Engineering Professional Certificate Capstone Project - Enterprise MLOps platform with automated pipelines
Code Analysis
6 files read · 3 roundsA disjointed collection of ML training logic (Flask) and a separate serving API (FastAPI) that are not integrated, alongside a disconnected Streamlit dashboard.
Strengths
The core training pipeline in ml_platform.py demonstrates solid implementation of synthetic data generation, model comparison, and SQLite-based registry. The code is readable and uses standard libraries effectively for educational purposes.
Weaknesses
Critical architectural inconsistency between Flask (training) and FastAPI (serving), lack of integration between components, trivial unit tests that do not cover the ML logic, and documentation that contradicts the actual implementation.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
32
Contributors
1
Files
28
Active weeks
7
Repository
Language
Python
Stars
1
Forks
0
License
MIT