MLOps - End 2 end pipeline

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_automl_best_run

Leverage Auto-ML generated notebook to build the best model out of the bpox.

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.

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_automl_champion

Leverage Auto-ML generated notebook to build the best model out of the bpox.

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.

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