Data Drift Courses and Certifications

Coursera

Validate, Analyze, and Monitor ML Models

Master ML model validation, A/B testing, shadow deployments, and drift monitoring using PSI to ensure safe releases and long-term model health in production environments.

• Coursera
• 2 hours 45 minutes
• Self-Paced
• Paid Course

Mitigate AI Risk and Ensure Ethical Operations

Explore practical techniques to detect bias, monitor model drift, and integrate AI governance with enterprise risk frameworks like ISO 31000, GDPR, and the EU AI Act.

• Coursera
• 3 hours
• Self-Paced
• Paid Course

No-Code Model Evaluation, Communication, and Business Impact

Master model evaluation beyond accuracy, testing against changing data and communicating insights for maximum business reliability and impact.

• Coursera
• 3 hours 18 minutes
• Self-Paced
• Paid Course

PMI-CPMAI™ 2026 Exam Prep | Pass on Your First Attempt

371 ratings at Udemy

Master PMI-CPMAI Exam. Confident preparation for the PMI-CPMAI™ 2026 certification. [NEW UPDATE FEBRUARY 2026]

• Udemy
• 5 hours 12 minutes
• Self-Paced
• Paid Course

MLOps.community

Implementing Data Capture for ML Observability and Drift Detection

Implement efficient data capture for ML observability, enabling monitoring and drift detection in complex ML systems with multiple data pipelines and transformations.

• YouTube
• 24 minutes
• Self-Paced
• Free Video

MLOps World: Machine Learning in Production

CyclOps: A Framework for Data Extraction, Model Evaluation and Drift Detection in Clinical Machine Learning

Explore CyclOps, a unified framework for healthcare ML, enabling data extraction, model evaluation, and drift detection in clinical settings. Streamline development of robust AI models.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

Alan Turing Institute

Dealing with Privacy, Bias and Drift in Synthetic Primary Care Data

Explore synthetic healthcare data generation, addressing privacy concerns, bias detection, and concept drift in evolving medical datasets for improved research and policy-making.

• YouTube
• 40 minutes
• Self-Paced
• Free Video

Amazon Web Services;  Amazon

AWS ML Engineer Associate 4.1 Monitor Model Performance and Data Quality

Learn advanced techniques for monitoring ML solutions using Amazon SageMaker, including detecting data drift, model quality issues, bias, and feature attribution drift. Explore tools like Model Monitor, Clarify, and Model Dashboard.

• AWS Skill Builder
• 2 hours 30 minutes
• Self-Paced
• Paid Course

The Full Stack

Monitoring ML Models - Full Stack Deep Learning - Spring 2021

Learn techniques for monitoring ML models in production, including detecting data drift, measuring changes, and using tools to maintain model health and performance over time.

• YouTube
• 37 minutes
• Self-Paced
• Free Video

Conf42

The Next Frontier - Observability in ML Systems

Explore the frontier of ML systems observability, covering key components, challenges, and future trends in various domains including LLMs and computer vision.

• YouTube
• 15 minutes
• Self-Paced
• Free Video

Kode Kloud

What is MLOps? - Introduction to Machine Learning Operations with MLflow

Discover MLOps fundamentals, model drift solutions, and MLflow implementation through hands-on labs covering experiment tracking, hyperparameter tuning, and production deployment.

• YouTube
• 13 minutes
• Self-Paced
• Free Video

Conf42

Why Good Models Fail After Deployment

1 rating

Discover why ML models fail in production through 5 key failure modes: data drift, concept drift, label drift, pipeline issues, and feedback loops. Learn monitoring and retraining strategies.

• YouTube
• 25 minutes
• Self-Paced
• Free Video

Data Science Dojo

How to Detect Silent Failures in ML Models

Learn techniques to detect and address silent failures in machine learning models, including data drift detection, performance estimation, and monitoring strategies, without relying on ground truth data.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

Data Science Dojo

Building and Maintaining High-Performance AI

Learn strategies for developing and maintaining high-performance AI models, including testing methods, drift detection, and systematic debugging. Gain insights on model monitoring and iteration for optimal performance.

• YouTube
• 51 minutes
• Self-Paced
• Free Video

USENIX

Ekya - Continuous Learning of Video Analytics Models on Edge Compute Servers

Continuous learning for edge video analytics models, addressing data drift and balancing inference-retraining tradeoffs. Ekya optimizes resource allocation for improved accuracy with fewer GPU resources.

• YouTube
• 17 minutes
• Self-Paced
• Conference Talk