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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

  • 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

  • Introduction to workflows orchestration
  • Introduction to Prefect
  • From notebooks to workflows
  • Continuous Training
  • Next steps and resources
  • Practice
  • Homework

Module 3: Data Management

  • Testing Data Quality
  • Creating Data Pipelines
  • Feature Stores
  • Data Leakage
  • Training / Serving Skew
  • Practice
  • Homework

Module 4: 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

  • 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

  • End-to-end project with all the things above

Instructors

  • El Hamri Amale
  • Guidi Ariel
  • Quinquet Guillaume
  • Si Larbi Karim
  • Deschamps Mael