mage-ai/machine_learning

The definitive end-to-end machine learning (ML lifecycle) guide and tutorial for data engineers.

30
/ 100
Emerging

This guide helps data engineers build and manage machine learning models from start to finish. It takes raw business problems and data, then walks you through preparing that data, training and evaluating models, deploying them for predictions, and continuously monitoring and retraining them. This is for data engineers responsible for the entire lifecycle of a machine learning system.

No commits in the last 6 months.

Use this if you are a data engineer looking for a comprehensive, end-to-end tutorial to implement a machine learning workflow.

Not ideal if you are looking for a high-level overview or are not a data engineer involved in the technical implementation of ML systems.

data engineering MLOps machine learning deployment data pipeline management model lifecycle
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 8 / 25
Community 16 / 25

How are scores calculated?

Stars

23

Forks

6

Language

Python

License

Last pushed

Nov 14, 2024

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/mage-ai/machine_learning"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.