Welcome to Our Final Course on Advanced Deployment Scenarios
As we conclude our final course on advanced deployment scenarios, you've finished three of the four causes of this specialization on de trans employments. It's time to dive deeper into several advanced topics that will help you improve your skills and take your model deployment to the next level.
First up, let's explore tentacle serving. This is an essential topic that will teach you how to serve your models over HTTP and HTTPS. By mastering this skill, you'll be able to deploy your models seamlessly and efficiently, making it easier for others to use and interact with them. With tentacle serving, you'll gain the ability to share your model's metadata with others, allowing them to inspect, debug, and even collaborate on your work.
Next, we'll delve into TensorFlow Hub, a repository that allows you to take models from existing sources and use them in your own projects. By leveraging TensorFlow Hub, you can tap into a vast library of pre-trained models and fine-tune them for your specific needs. This is especially useful when it comes to transfer learning, where you can extract layers from one model and incorporate them into another, saving time and resources.
Another critical aspect of our course is TensorBoard, a visualization tool that provides insights into the performance and behavior of your model during training. By using TensorBoard, you'll be able to monitor your model's progress in real-time, identify potential issues before they become major problems, and make data-driven decisions to improve its overall performance.
In addition to TensorBoard, we'll also explore a new feature called TensorBoard Death, which allows you to deploy the metadata of your model, providing a URL that others can use to inspect and debug your work. This is an exciting development that will enable researchers, developers, and enthusiasts to collaborate more effectively on model deployment.
Finally, we'll discuss Federated Learning, a topic that's both fascinating and crucial for deploying models in the wild. When you've trained a machine learning model and want to deploy it in a production environment, you need to consider how to scale its performance while maintaining its accuracy. This is where Federated Learning comes into play – an approach that enables multiple agents to collaborate on a common task without sharing their training data.
During this course, we'll explore the concept of Tensor Field Survey, which makes it easier for developers to deploy models in the cloud by providing a suite of tools and services that simplify the process. By leveraging Tensor Field Survey, you'll be able to set up an API, manage model updates, and handle versioning with ease – all of which will make your life as a developer much simpler.
Our goal is to reduce friction for developers so they can focus on serving infrastructure rather than dealing with tedious tasks like saving models in different directories or updating servers offline. By mastering these advanced deployment scenarios, you'll gain the skills and confidence needed to deploy high-quality models that meet the demands of modern applications. Stay tuned for the next part of our course!
"WEBVTTKind: captionsLanguage: enyou 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 infrastructureyou 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\n"
