课程: AutoML: Build Production-Ready Models Quickly!
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Cleaning up your resources - Python教程
课程: AutoML: Build Production-Ready Models Quickly!
Cleaning up your resources
- [Narrator] We are now familiar with Custom Vision and how we can use quick or advanced training to build image classifiers. Keep in mind that we can also build object detection models using Custom Vision. We have also learned to publish our training models and interact with their endpoints through HTTP requests to obtain predictions for new images. We did all of this using the Custom Vision resource that we created at the start of this chapter. This resource will incur costs for storage of our data set, compute time used to train our models, and end predictions. To avoid unnecessary costs, we must clean up these resources when we are no longer using them. So let's start by unpublishing one of our training iterations. This means we will no longer be able to obtain predictions via HTTP requests. We'll start by clicking "unpublish" for our advanced training, and "unpublish" for our quick training. Then, I'll head over to…
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内容
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Computer vision: How do you handle image data?4 分钟 26 秒
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Introducing Azure Custom Vision4 分钟 13 秒
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Uploading and labeling images4 分钟 29 秒
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Training and validating your model5 分钟 27 秒
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Using your model4 分钟 58 秒
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Working with code: Tradeoffs3 分钟 13 秒
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Challenge: Multi-class classification56 秒
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Solution: Multi-class classification5 分钟 2 秒
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Cleaning up your resources2 分钟 52 秒
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