Identify Damaged Car Parts with Vertex AutoML Vision
This is a self-paced lab that takes place in the Google Cloud console. Vertex AI brings together the Google Cloud services for building ML under one, unified UI and API. In Vertex AI, you can now easily train and compare models using AutoML or custom code training and all your models are stored in one central model repository. These models can now be deployed to the same endpoints on Vertex AI. AutoML Vision helps anyone with limited Machine Learning (ML) expertise train high quality image classification models.
Monthly Subscription Starting at AED 99 + VAT
- Level Professional
- Duration 2 hours
- Course by Google Cloud
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This is a self-paced lab that takes place in the Google Cloud console. Vertex AI brings together the Google Cloud services for building ML under one, unified UI and API. In Vertex AI, you can now easily train and compare models using AutoML or custom code training and all your models are stored in one central model repository. These models can now be deployed to the same endpoints on Vertex AI. AutoML Vision helps anyone with limited Machine Learning (ML) expertise train high quality image classification models. In this hands-on lab, you will learn how to produce a custom ML model that automatically recognizes damaged car parts. Once you’ve produced your ML model, it’ll be immediately available for use. You can use the UI or the REST API to start generating predictions directly from the Google Cloud Console.Modules
Identify Damaged Car Parts with Vertex AutoML Vision
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- Identify Damaged Car Parts with Vertex AutoML Vision
Auto Summary
Discover how to identify damaged car parts using Vertex AutoML Vision in this engaging data science and AI course. Guided by Coursera, this professional-level, self-paced lab on Google Cloud Console allows you to train high-quality image classification models, even with limited ML expertise. Lasting 120 minutes, the course offers both Starter and Professional subscription options, making it accessible for learners aiming to enhance their machine learning skills efficiently.
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