https://github.com/mxagar/mlops_udacity

These are my notes of the Udacity Nanodegree Machine Learning DevOps Engineer.
https://github.com/mxagar/mlops_udacity

cicd deployment dvc fastapi heroku machine-learning mlflow mlops model-drift monitoring production wandb

Last synced: 3 months ago
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These are my notes of the Udacity Nanodegree Machine Learning DevOps Engineer.

• Host: GitHub
• URL: https://github.com/mxagar/mlops_udacity
• Owner: mxagar
• Created: 2022-05-11T07:06:29.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2024-03-27T07:07:13.000Z (11 months ago)
• Last Synced: 2024-04-24T12:14:21.621Z (10 months ago)
• Topics: cicd, deployment, dvc, fastapi, heroku, machine-learning, mlflow, mlops, model-drift, monitoring, production, wandb
• Language: Jupyter Notebook
• Homepage:
• Size: 35.2 MB
• Stars: 8
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# Machine Learning DevOps Engineer: Personal Notes on the Udacity Nanodegree

These are my notes of the [Udacity Nanodegree Machine Learning DevOps Engineer](https://www.udacity.com/course/machine-learning-dev-ops-engineer-nanodegree--nd0821).

The nanodegree is composed of four modules:

1. [Clean Code Principles](./01_Clean_Code/MLOpsND_CleanCode.md)

2. [Building a Reproducible Model Workflow](./02_Reproducible_Pipelines/MLOpsND_ReproduciblePipelines.md)

3. [Deploying a Scalable ML Pipeline in Production](./03_Deployment/MLOpsND_Deployment.md)

4. [ML Model Scoring and Monitoring](./04_Monitoring/MLOpsND_Monitoring.md)

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](https://github.com/mxagar/customer_churn_production).

2. A Reproducible Machine Learning Pipeline for Short-Term Rental Price Prediction in New York City: [ml_pipeline_rental_prices](https://github.com/mxagar/ml_pipeline_rental_prices).

3. Deploying a Machine Learning Model on Heroku with FastAPI: [census_model_deployment_fastapi](https://github.com/mxagar/census_model_deployment_fastapi).

4. A Dynamic Risk Assessment System — Monitoring of a Customer Churn Model: [churn_model_monitoring](https://github.com/mxagar/churn_model_monitoring).

Mikel Sagardia, 2022.  

No guarantees.