agent-platform-tuning

To fine-tune open models or Gemini models on Google Cloud Agent Platform, you must set up environment authentication, prepare datasets for Cloud Storage, submit a tuning job, and deploy the resulting model. The process is guided end-to-end.

Model tuning on Agent Platform supports datasets formatted using pandas and pyarrow. You must validate your dataset and upload it to Google Cloud Storage before submitting the tuning job for the selected open or Gemini model.

Yes, you can tune open models using Google Cloud AI Platform infrastructure. The process involves configuring IAM permissions, selecting appropriate model hyperparameters, monitoring the tuning job via Cloud Console, and deploying the fine-tuned model endpoint.

Yes, numpy and pandas are required dependencies for dataset preparation before Agent Platform tuning. You use them to validate and format your training data before uploading it to Google Cloud Storage for the model tuning job execution.

The best way to monitor a model tuning job in Google Cloud is by tracking its status via the Cloud Console link. The platform also supports background task execution for periodic polling to check job progress until completion.

To deploy a fine-tuned model after the Agent Platform tuning job completes successfully, you follow the deployment steps provided by the platform. This transitions your newly tuned Gemini or open model into an active, usable endpoint.