Join Sahil Malik for an in-depth discussion in this video What you should know, part of Azure Machine Learning Development: 3 Deploying and Managing Models.
- [Instructor] Let's talk a little bit about what you should know, your background so you get the most value out of this course. You need to have some basic AI concepts. You don't need to be an expert, but at least some basic AI concepts that I've covered in my other LinkedIn courses about ML Studio. That much should be enough. You should also have some familiarity with Azure ML Studio itself. In this course, I won't be showing you how to create a model or the details of a model, we'll simply just pick an existing model and run with it.
Because the focus of this course is the lifecycle of that model and managing that model. You will need a modern browser and you will need a Microsoft account, also known as Live ID. And yes, you shall also need an Azure subscription. You can work on any machine, Mac, Windows, Linux, all of them are welcome. So if you have those resources, let's get started.
- Creating a machine learning workspace
- Creating and training an experiment
- Creating a predictive experiment
- Deploying an experiment as a web service
- Enabling logging
- Viewing logs for diagnostic purposes
- Scale and geographic deployment of your service
- Using machine learning with API management