01-mlops-quickstart
00_mlops_end2end_quickstart_presentation
Understand MLOps and the flow we'll implement for Customer Churn detection.
01_feature_engineering
Create and save your features to Feature store.
02_train_lightGBM
Leverage XGBoost to build a first ML model.
03_from_notebook_to_models_in_uc
Leverage MLFlow to find your best training run and save as Challenger
04_challenger_validation
Test your challenger model and move it as Champion.
05_batch_inference
Leverage your ML model within inference pipelines.
02-mlops-advanced
00_mlops_end2end_advanced
Understand MLOps and the flow we'll implement for Customer Churn detection.
01_feature_engineering
Create and save your features to Feature store.
02_model_training_hpo_optuna
Leverage Optuna to fine tune hyperparameter and deploy a new model.
03a_create_deployment_job
Leverage MLFlow to find your best training run and save as Challenger
03b_from_notebook_to_models_in_uc
Deploy the model to UC
04a_challenger_validation
Test your challenger model and move it as Champion.
04b_challenger_approval
New model approval.
05_batch_inference
Leverage your ML model within inference pipelines.
06_serve_features_and_model
Create online table & serve model in a serverless endpoint
07_model_monitoring
Leverage lakehouse monitoring to monitor inference table for drifts.
08_drift_detection
Create synthetic data and detect drift