Designing Frameworks for Reliability in Deep Learning Systems

There has been a great amount of progress in deep learning models the last decade. Such are most accurate when applied to test data drawn from same distribution as their training set. However, practice, confronting real-world settings rarely match distribution. This study explores use co-design approaches for developing reliable design frameworks systems. It aims raise awareness on how develop ML within context recommender While much work needs be done this field, provides suggestions and practical tips such case systems