How Observability Uncovers the Effects of ML Technical Debt

Observability is crucial for measuring the negative effects of technical debt in machine learning production systems. Many teams lack tools and processes to evaluate their ML models' ongoing performance after deployment, leading to potential issues going unnoticed until customers report them. Technical debt can be silent and insidious, causing catastrophic failures without triggering typical DevOps alarms on service and data availability. Observability into the dynamics of your data and models allows for proactive detection and response to changes in model performance before stakeholders or customers notice an issue. AI observability platforms like WhyLabs Observatory and open-source data logging libraries such as whylogs can provide purpose-built tools for large datasets, helping teams uncover the sources of ML technical debt effects and improve their models' performance.