The objective of the proposed research is to develop a variety of novel memory forensic techniques, which automatically recover the unique deployment model and rehosts it in a lab environment such that these models can be investigated. These techniques navigate through both main memory and GPU memory spaces to recover complex ML data structures, using recovered Python objects to guide the recovery of lower-level C objects, ultimately leading to the recovery of the uniquely refined model. Then these using my technique, we are able to rehost the model within the investigator's device, where the investigator can apply various white-box testing methodologies. We have evaluated these techniques using three versions of TensorFlow and PyTorch with the CIFAR-10, LISA, and IMDB datasets. We have recovered 30 models from main memory and GPU memory with 100% accuracy and rehosted them into a live process successfully.