Machine Learning DevOps Engineer: Personal Notes on the Udacity Nanodegree

These are my notes of the Udacity Nanodegree Machine Learning DevOps Engineer.

The nanodegree is composed of four modules:

1. Clean Code Principles
2. Building a Reproducible Model Workflow
3. Deploying a Scalable ML Pipeline in Production
4. ML Model Scoring and Monitoring

Each module has a folder with its respective notes; you need to go to each module folder and follow the Markdown file in it.

Projects

Udacity requires the submission of a project for each module; these are the repositories of the projects I submitted:

1. Predicting Customer Churn with Production-Level Software: customer_churn_production.
2. A Reproducible Machine Learning Pipeline for Short-Term Rental Price Prediction in New York City: ml_pipeline_rental_prices.
3. Deploying a Machine Learning Model on Heroku with FastAPI: census_model_deployment_fastapi.
4. A Dynamic Risk Assessment System — Monitoring of a Customer Churn Model: churn_model_monitoring.

Mikel Sagardia, 2022.
No guarantees.