https://github.com/artefactory/supinfo_mlops_course
https://github.com/artefactory/supinfo_mlops_course
Last synced: 5 months ago
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- Host: GitHub
- URL: https://github.com/artefactory/supinfo_mlops_course
- Owner: artefactory
- License: mit
- Created: 2023-01-05T09:54:18.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2025-03-20T20:47:10.000Z (over 1 year ago)
- Last Synced: 2025-07-09T16:07:50.850Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 3 MB
- Stars: 3
- Watchers: 2
- Forks: 16
- Open Issues: 2
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Overview
### Objective
Teach practical aspects of productionizing ML services — from collecting requirements to model deployment and monitoring.
### Target audience
Data scientists and ML engineers. Also software engineers and data engineers interested in learning about putting ML in production.
### Pre-requisites
* Python
* Docker
* Being comfortable with command line
* Prior exposure to machine learning
* Prior programming experience (at least 1+ year)
### Timeline
Course start: March 8th
Course end: March 16th
Final project presentation: March 31st
## Syllabus
### [Module 1: Introduction](lessons/01-intro)
* What is MLOps
* Why do we need MLOps
* Running example: NY Taxi trips dataset
* Course overview
* Environment preparation
* Practice
* Homework
### [Module 2: Pipeline and Orchestration](lessons/02-pipeline-and-orchestration)
* Introduction to workflows orchestration
* Introduction to Prefect
* From notebooks to workflows
* Continuous Training
* Next steps and resources
* Practice
* Homework
### [Module 3: Data Management](lessons/03-data-management)
* Testing Data Quality
* Creating Data Pipelines
* Feature Stores
* Data Leakage
* Training / Serving Skew
* Practice
* Homework
### [Module 4: Model and Experiment Management](lessons/04-model-and-experiment-management)
* Experiment tracking intro
* What is MLflow
* Experiment tracking with MLflow
* Saving and loading models with MLflow
* Model registry
* Practice
* Homework
### [Module 5: Model Deployment](lessons/05-model-deployment)
* Web service: model deployment with FastAPI
* Docker: containerizing a web service
* MLflow: retrieving a model from the model registry
* Locust: load testing a web service
### [Module 6: Practice](lessons/06-practice)
* End-to-end project with all the things above
## Instructors
- El Hamri Amale
- Guidi Ariel
- Quinquet Guillaume
- Si Larbi Karim
- Deschamps Mael