Efficient Serverless deployment of PyTorch models on Azure

Recent advances in deep learning and cloud-based infrastructure have led to innovations in models for various domains like natural language processing, computer vision, recommendations. Of course, developing the model is only half the story. Your models are mostly useful once they are served up for making predictions for consumption in in AI-driven scenarios from the end applications. It is important to do it in a cost-effective and reliable manner. However, managing infrastructure for hosting your models is challenging as it involves several aspects like maintaining your fleet, ensuring reliability, scaling, security and ongoing monitoring and management.