Overview

Objective

Teach practical aspects of productionizing ML services — from collecting requirements to model deployment and monitoring.

[MANDATORY] Pre-requisites & setup

Note

TL; DR

Docker Desktop
Git
Conda / Minconda / another Python environment manager
Python 3.10
Install ./requirements.txt

You can find all pre-requisites and setup instructions here.

Timeline

Course start: October 19th Course end: October 23th

Syllabus

Module 1: Introduction to MLOps

What is MLOps
Course overview
Coding best practices
Prerequisites and setup
Running example: NY Taxi trips dataset

Module 2: Experiment Tracking

Experiment tracking intro
What is MLflow
Experiment tracking with MLflow
Saving and loading models with MLflow
Model registry
Practice

Module 3: Model Deployment

Web service: model deployment with FastAPI
Docker: containerizing a web service
Practice

Module 4: Pipelines and Orchestration

Tasks, Flows, Deployments
From notebooks to Workflows
Workflows orchestration with prefect
Practice

[Module 5: Life Cycle Management]

Model monitoring
Model retraining
Concept drift
Data drift & data management

[Module 6: Project]

End-to-end project with all the things above

Instructors

DELATTRE Bruce
BRITO Henrique
BERTRAND Jules
SERRA Luca