BaşlayınÜcretsiz Başlayın

Implementing a monitoring workflow

Throughout the course, you've learned about the monitoring workflow. The first step is performance monitoring. If there are negative changes, the next steps involve multivariate drift detection to identify if drift caused the performance drop, followed by univariate drift detection to pinpoint the cause in individual features. Once the investigation results are in, you can take steps to resolve the issue.

To solidify this knowledge, in the exercise, you'll apply this process to the US Consensus dataset. The reference and analysis datasets are pre-loaded, and you have access to the CBPE estimator, uv_calc univariate calculator, and an alert_count_ranker for feature drift ranking.

Bu egzersiz

Monitoring Machine Learning in Python

kursunun bir parçasıdır
Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Estimate the performance
estimator.____(____)
estimated_results = estimator.____(____)
estimated_results.____.____
Kodu Düzenle ve Çalıştır