Google Vertex AI

Step-by-Step Guide for Google Vertex AI

1. Open your browser and navigate to the Google Cloud Console:
   https://console.cloud.google.com/vertex-ai.
   Log in using your Google Cloud account credentials.
2. Enable Vertex AI API:
   • Once logged in, if this is your first time using Vertex AI, you may need to enable the Vertex AI API.
   • Go to the API & Services dashboard and search for Vertex AI. Enable it for your project.
3. Create a New Project:
   • In the Google Cloud Console, click the Project Selector (next to the Google Cloud logo at the top).
   • Click on New Project, provide a project name, organization (if needed), and a billing account. Click Create to initialize your project.
4. Access Vertex AI:
   • After creating the project, in the left-hand menu, click on AI & Machine Learning and select Vertex AI from the dropdown.
5. Create a New Vertex AI Model:
   • In the Vertex AI dashboard, click Create New to start building a model.
   • Select AutoML or Custom Training based on your needs:
     • AutoML: For beginners, choose AutoML to let Google automatically train and optimize your model.
     • Custom Training: If you have your own custom models or datasets, you can choose this option for more advanced use cases.
6. Import Your Data:
   • Select Dataset from the Vertex AI menu. Click on Create Dataset.
   • Choose your data type (e.g., Tabular, Image, Text, or Video) and upload your dataset from your local machine or use data stored in Google Cloud Storage.
7. Train Your Model:
   • After your data is uploaded, click on Train New Model. Choose a training method (AutoML or custom model training).
   • Define the target (what you’re trying to predict), configure the training options, and start the training process.
8. Deploy Your Model:
   • Once the model is trained, go to the Models section in the Vertex AI dashboard.
   • Click Deploy and specify the compute resources for serving your model (e.g., instance type and region).
   • After deployment, Vertex AI will provide you with an endpoint for making predictions.
9. Make Predictions:
   • After deployment, you can test your model by making real-time predictions from the Prediction tab.
   • Alternatively, you can use the provided REST API endpoint to integrate Vertex AI predictions into your application.

Additional Resources for Google Vertex AI:

1. Google Vertex AI Documentation:
   https://cloud.google.com/vertex-ai/docs
2. Google Cloud Console (Vertex AI):
   https://console.cloud.google.com/vertex-ai
3. Getting Started with AutoML:
   https://cloud.google.com/vertex-ai/docs/start/automl-users

Custom Model Training:
https://cloud.google.com/vertex-ai/docs/training