In previous chapters, we saw how to use some notable Deep Learning (DL) platforms, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure, to enable DL in our web applications. We then saw how to make websites secure using DL. However, in production, the challenge is often not just building the predictive model—the real problems arise when you want to update a model that is already sending responses to users. How much time and business can you lose in the 30 seconds or 1 minute that it may take to replace the model file? What if there are models customized for each user? That might even mean billions of models for a platform such as Facebook.
You need to have definite solutions for updating models in production. Also, since the ingested data may not be in the format that the training is performed in, you...
