Recent advances in machine learning have introduced a new lens to envision novel solutions in many research domains and the Electronic Design Automation field is an evident example. Today, Machine Learning research is penetrating into the different stages of the Integrated Circuits design cycles equipped with accurate and fast models. However, addressing the applicability of learned models within the ever-changing design environment has not received enough study. In this work, we propose ADAPT as a framework for the fast migration of machine learning models. Towards this end, an unsupervised Bayesian-based accuracy estimation method is used. Moreover, different techniques for learning with small datasets are adopted to build a complete migration framework. The efficacy of ADAPT, both in terms of accelerating model migration and accurate estimations, is demonstrated by using lithography hotspot detection as a case study.