Explore an in-depth tutorial on Evidently AI, an open-source Python library for ML model monitoring and observability. Learn how to evaluate, test, and monitor data and machine learning models throughout their lifecycle, from validation to production. Discover Evidently's modular approach, including Reports, Test Suites, and Monitoring Dashboard, and its application to tabular and text data. Gain hands-on experience with practical demonstrations of model monitoring, performance checks, and target drift analysis using Evidently AI's simple, declarative API and built-in metrics, tests, and visualizations.
Overview
Syllabus
Introduction
What is Evidently AI
Model Monitoring Using Evidently AI
Model Performance Check Using Evidently AI
Target Drift Using Evidently AI
Taught by
Krish Naik
Related Courses
-
Monitoring Machine Learning Concepts
-
MLOps1 (Azure): Deploying AI & ML Models in Production using Microsoft Azure Machine Learning
-
AI Quality Workshop: How to Test and Debug ML Models
-
Introduction to Machine Learning Models (AI) Testing
-
Build and operate machine learning solutions with Azure Machine Learning
-
Monitoring Machine Learning in Python
