Integrating AI and machine learning (AI/ML) into Continuous Delivery (CD) pipelines streamlines the process of verifying production deployments by automating tasks traditionally done manually, thus enhancing reliability and performance monitoring. Harness employs unsupervised machine learning to automate the verification steps in deployment pipelines, drastically reducing manual effort and allowing organizations to better understand the business impact of their deployments. By leveraging data from tools like AppDynamics, Splunk, and New Relic, Harness enables real-time analysis of key performance indicators (KPIs) such as business revenue, application performance, and resource utilization. This AI-driven approach identifies performance and quality regressions, such as anomalies or failures, and can initiate automatic rollbacks, ensuring deployments are both efficient and reliable. Additionally, developers can provide human feedback to improve the accuracy of the machine learning models, making the process more precise over time.