Agile development teams must prioritize observability and monitoring for microservices and applications while leveraging AIOps for efficient incident management. As user feedback drives enhancements, DevOps teams must continuously upgrade applications to prevent technical debt. Machine learning model lifecycle management presents unique complexities, differing from standard software development due to the unpredictable nature of data. Effective monitoring is essential for maintaining model performance, particularly in high-variability datasets, allowing teams to troubleshoot and improve their models for consistent AI prediction outcomes.
Monitoring can help businesses balance the benefits of AI predictions with their need for predictability and trust in automated decisions, ensuring models perform effectively.
Model development life cycle resembles software development life cycle from a high level, but with much more complexity. Data, the foundation of an ML model, is unpredictable.
#agile-development #microservices #machine-learning #monitoring #aiops #model-monitoring #devops #technical-debt
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