It is necessary to mantain an online attribute on a Feature Store of Vertex AI to serve an endpoint model to provide real-time predictions??
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I was trying to figure out key differences between using GCP Vertex AI feature store and Saving preprocessed features to BigQuery and loading whenever it gets necessary.
I still cannot understand why to choose the first option, rather than the second option, which seems to be easier and more accessible.
Is there any good reason to use feature store in Vertex AI, rather than storing features in BigQuery tables formats?
Vertex AI Feature Store and BigQuery, both can be used to store the features as mentioned by you. But Vertex AI Feature Store has several advantages over BigQuery that makes it favorable for storing features.
Advantages of Vertex AI Feature Store over BigQuery :
Vertex AI Feature Store is designed to create and manage featurestores, entity types, and features whereas BigQuery is a data warehouse where you can perform analysis on data.
Vertex AI Feature Store can be used for batch and online storage but BigQuery is not a solution for storage.
Vertex AI Feature Store can be used for sharing the features across the organization from the central repository which BigQuery does not provide.
Vertex AI Feature Store is a managed solution for online feature serving which is not supported by BigQuery.
For more information, you can check this link.
What is the difference between Feature Store and Dataset inside of Vertex AI (GCP)?
And why the Feature Store has Offline and Online serving nodes? What is it for?
As described at the official documentation of Vertex AI's Feature Store
, a feature store is a container for organizing, storing, and serving ML feature. Basically its a more organized container that can be easily store or share features to permitted users. I would suggest reading the article linked above.
Online serving nodes is best described here:
"Online serving nodes provide the compute resources used to store and serve feature values for low-latency online serving."
I have created a Vertex AI AutoML image classification model. How can I assess it for overfitting? I assume I should be able to compare training vs validation accuracy but these do not seem to be available.
And if it is overfitting,can I tweak regularization parameters? Is it already doing cross validation? Anything else that can be done? (More data,early stopping, dropouts ie how can these be done?)
Deploy it to endpoint and test result with sample images by uploading to endpoint. If it's overfitting you can see the stats in analysis. You can increase the training sample and retrain your model again to get better result.
I need to train a custom OCR in vertex AI. My data with have folder of cropped image, each image is a line, and a csv file with 2 columns: image name and text in image.
But when I tried to import it into a dataset in vertex AI, I see that image dataset only support for classification, segmentation, object detection. All of dataset have fixed number of label, but my data have a infinite number of labels(if we view text in image as label), so all types doesn't match with my requirement. Can I use vertex AI for training, and how to do that ?
Since Vertex AI managed datasets do not support OCR applications, you can train and deploy a custom model using Vertex AI’s training and prediction services.
I found a good article on building an OCR system from scratch. This OCR system is implemented in 2 steps
Text detection
Text recognition
Please note that this article is not officially supported by Google Cloud.
Once you have tested the model locally, you can train the same on Vertex AI using the custom model training service. Please follow this codelab for step-by-step instructions on training and deploying a custom model.
Once the training is complete, the model can be deployed for inference using a pre-built container offered by Vertex AI or a custom container based on your requirements. You can also choose between batch predictions for synchronous requests and online predictions for asynchronous requests.
I have been using the Vertex AI training service with a custom container for my own machine learning pipeline. I would like to get tensorboard logs into the experiments tab to see in real-time the metrics while the model is training.
I was wondering if it is possible to set a custom training job in the user interface setting a TENSORBOARD_INSTANCE_NAME. It seems that this is only possible through a json-post-request.