Summary
In this chapter, we created an end-to-end vision-based solution to detect visual defects from images. We saw how CNN-based deep learning architectures can be used to extract useful features from images and then use those features for tasks such as classification. After training and testing our model, we went ahead and deployed it to a Vertex AI endpoint, allowing it to serve online or on-demand prediction requests for any number of downstream applications.
After completing this chapter, you should be confident about how to approach vision-based problems and how to utilize ML to solve them. You should now be able to train your own vision-based classification models to solve real-world business problems. After completing the second section on deploying a custom model to a Vertex AI endpoint and the third section on getting online prediction from a Vertex endpoint, you should now be able to make your custom vision models usable for any downstream business application, by deploying...
