https://github.com/astrojuanlu/workshop-from-zero-to-mlops

Workshop "From zero to MLOps: An open source stack to fight spaghetti ML"
https://github.com/astrojuanlu/workshop-from-zero-to-mlops

Last synced: 17 days ago
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Workshop "From zero to MLOps: An open source stack to fight spaghetti ML"

• Host: GitHub
• URL: https://github.com/astrojuanlu/workshop-from-zero-to-mlops
• Owner: astrojuanlu
• Created: 2024-06-24T07:46:22.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-07-08T11:44:10.000Z (6 months ago)
• Last Synced: 2024-12-17T02:42:03.887Z (27 days ago)
• Language: Jupyter Notebook
• Size: 1.85 MB
• Stars: 24
• Watchers: 2
• Forks: 5
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# From zero to MLOps: An open source stack to fight spaghetti ML

![Powered by Kedro](https://img.shields.io/badge/powered_by-kedro-ffc900?logo=kedro)

Material for the workshop "From zero to MLOps: An open source stack to fight spaghetti ML"

first delivered at EuroPython 2024 https://ep2024.europython.eu/session/from-zero-to-mlops-an-open-source-stack-to-fight-spaghetti-ml

[![Open in Gitpod](https://gitpod.io/button/open-in-gitpod.svg)](https://gitpod.io/#https://github.com/astrojuanlu/workshop-from-zero-to-mlops/)

![Open source MLOps stack](img/kedro-mlops.png)

## Outline

Approximate duration: 3 hours.

0. What is even MLOps? (10m)

1. Initial setting: Jupyter notebook, local files, everything mostly works! (20m)

2. Your local lakehouse with MinIO and Delta (10m)

3. Data and ML pipelines with Kedro (50m)

4. Break (15m)

5. Experiment tracking and model registry with MLFlow (20m)

6. Orchestration with Prefect (15m)

7. Scaling execution on a Ray cluster (15m)

8. Model monitoring with Evidently (15m)

9. Conclusions and buffer (25m)