How Not to Let Your Data and Model Drift Away Silently

Explore strategies for maintaining the effectiveness of machine learning models in production environments in this 40-minute conference talk from MLOps World. Learn about the critical importance of monitoring and testing deployed ML models to ensure they continue to deliver expected results and business value. Gain insights from Chengyin Eng, a Data Science Consultant at Databricks, on best practices for detecting and addressing data and model drift, which can silently erode model performance over time. Discover techniques to keep your ML models robust and reliable as they operate in real-world conditions, maximizing their impact and longevity.