Four advanced deployment scenarios (TensorFlow: Data and Deployment)

Key Takeaways

you finished three of the four causes of this specialization on de trans employments welcome to this final course on advanced deployment scenarios in this course you're going to learn about several advanced topics what are the advanced topics oh we've got so many to choose from right so we're gonna start with a tentacle serving so you can learn how to serve your models over HTTP and HTTPS then we're gonna be looking at tensorflow hub so that's a repository for models that somebody can take models from and use the entire model and maybe use transfer learning to take layers from that model then this tensor board and a new part of tensor board called tensor board death where you can actually deploy the metadata about your model and you'll get a URL back that you can then share so other people can look at your model can inspect and maybe help you debug your model and then finally we'll end up with the one that I'm really most excited about and that's to talk about federated learning so when you have your model deployed in the wild how do you then effectively get federated learning from that the person this week will start to have tensor field survey yeah found it after you've trained a machine there any moderate deep learning model is sometimes so many steps to take them although packages out posted by Carlos the server maintain the cloud hosting server and you set up an API so that you or someone else can call your model to get predictions back so anything that tessa field provides to make all those steps easier it just makes life easier for developers exactly and that's the whole idea behind this and then also model versioning right as you retrain your model and you save it out in a new directory you can serve from that one and you can have multiple models and handle your model versioning like that we try to make that as easy as possible for the developer so that you can deploy something to cloud hosting server and when you version it push the new model and have that you know just work without too much messing around with saving models in different directories and copy and pasting in right days and hope people do this exactly or taking a server offline to update its you know those kind of things we you know the goal is to really reduce the friction for developers so that they can have a serving infrastructure