Today Machine learning can be seen in literally every aspect in life. The question arises about applying machine learning to GitOps! Can we use machine learning to GitOps so that we can increase the chances of improving the deployment accuracy and make predictions on which deployments are likely to fail and need more attention? Is it possible to use machine learning to improve the some of the existing quality metrics and drive for better deployment results? Can machine learning be used to improve the deployment validation processes? Get an answer by following this talk