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Code exercise with verifiable output
·
Module 1
Project Foundation and Application Scaffolding
Project Setup: GitHub, DagsHub, and Dependencies
Set up the project environment: create a Git repo on GitHub, connect it to DagsHub for MLflow/DVC, and install dependencies.
DVC and DagsHub: Tracking the H&M Dataset
Download the H&M Kaggle dataset, initialize DVC with DagsHub as remote, and track the raw CSV files; return the DagsHub repo URL, row/column counts per CSV, and the .dvc file contents for articles.csv.
Flask Backend: Health Check & Skeleton Endpoints
Initialize a Flask backend with /health and skeleton /recommend endpoints; push to GitHub with .gitignore configured; return the repo URL and the /health JSON response.
Connecting Flask to MongoDB Atlas
Connect the Flask backend to a free MongoDB Atlas instance for future use; return confirmation of a successful connection test.
5
Frontend Dashboard Fundamentals
Build a frontend dashboard skeleton using React or vanilla HTML/CSS/JS, displaying placeholder product cards and a recommendation panel; serve locally.
6
Connecting Frontend to Flask: A Health Check
Establish basic communication between the frontend and the Flask backend, allowing the frontend to call the /health endpoint; return the file structure and a screenshot URL of the working dashboard.
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