mastering-mlops-exercises

Hands-on exercises for "Mastering MLOps: Complete course for ML Operations"

Exercise 1: Development of a model with Pycaret and registration in MLFlow

First hands-on exercise, developing a model to predict the churn variable for a company with a subscription-based business model, using Pycaret and registering experiments with MLFlow.

Code: exercise_1

Exercise 2: Repository with DagsHub

Second hands-on exercise, creating an online repository to be able to work with a data science team using MLOps practices.

Code: exercise_2

Exercise 3: Versioning dataset with DVC

Third hands-on exercise, downloading and modifying data and versioning it using DVC.

Code: exercise_3

Exercise 4: Shared MLFlow

Fourth hands-on exercise, registering Churn Classification experiments on MLFlow, but this time using a shared environment in DagsHub.

Code: exercise_4

Exercise 5: Develompment of BentoML Service

Exercise that consists in training a ML model to predict the type of flower using the Iris dataset, and developing a Service with the model, using BentoML.

Code: exercise_5