Data Drift Courses and Certifications

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
• On-Demand
• Free Video

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
• On-Demand
• 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
• On-Demand
• Free Video

Google Cloud Professional Machine Learning Engineer Cert Prep: 6 Monitoring, Optimizing, and Maintaining ML Solutions

7 ratings at LinkedIn Learning

Learn about monitoring, optimizing, and maintaining ML solutions in preparation for the Google Professional Machine Learning Engineer certification exam.

• LinkedIn Learning
• 1 hour 11 minutes
• On-Demand
• Free Trial Available

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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• Free Video

USENIX

Observability in the MLOps Lifecycle with Prometheus

Explore MLOps monitoring with Prometheus, covering ML deployment challenges, data drift, and model metrics. Learn to enhance existing systems and integrate with tools like Flyte, Seldon Core, and FastAPI.

• YouTube
• 24 minutes
• On-Demand
• Conference Talk

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
• On-Demand
• 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
• On-Demand
• Conference Talk

Securing ML Workloads with Kubeflow and MLOps - Pwned By Statistics

Learn to secure machine learning workloads using Kubeflow and MLOps. Explore techniques like model extraction, hidden data attacks, and leakage detection to protect your ML pipelines and models.

• YouTube
• 51 minutes
• On-Demand
• 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
• On-Demand
• Free Video

Open Data Science

Dataset Management for Computer Vision - Important Component to Delivering Computer Vision Solutions

Explore dataset management's crucial role in computer vision solutions. Learn to align business goals, express assumptions, and maintain datasets for successful real-world implementations.

• YouTube
• 41 minutes
• On-Demand
• Free Video

Prometheus in the MLOps Lifecycle

Explore Prometheus for monitoring ML deployments, addressing data drift, concept drift, and model metrics. Learn to enhance existing setups and integrate with Flyte, Seldon Core, or FastAPI.

• YouTube
• 28 minutes
• On-Demand
• Free Video