Bootstrap your own Handler: How and why to create custom handlers for PyTorch's TorchServe

TorchServe is a great tool to deploy trained PyTorch models, there is no denying that. But, as with any relatively new project, it is still creating a community around it to help with the more niche aspects of its implementation. As part of this community, we can contribute to this. So today, we will be discussing how to develop advanced custom handlers with PyTorch's TorchServe. We will also be reviewing the process of saving your PyTorch model with torch-model-archiver and how to include all the new artifacts created while we are at it.