My use case is related to multiclass image classification. Deployed CNN Model in production and enabled Model Monitoring for prediction drift detection only which does not require training data. It automatically gets created two buckets- analysis and predict in storage bucket. Then I created and run 1000 instances for model testing purpose(Same request 1000 times through Apache Bench) as it was prerequisite. I kept monitoring job to run for every hour and 100% sampling rate. I am not getting any output or logs in newly created buckets?
- What's the error here?
- Is Model Monitoring(Prediction Drift Detection) not enabled for Image Data by Vertex AI?
- What steps do I need to take in order to check the Model Monitoring is working fine for Image Classification Model. We need evidence in the form of logs generated in two buckets.
Model monitoring is only supported for tabular AutoML and tabular custom-trained models at the moment. It is not support for custom-trained image classification models.
For a more proactive approach that should minimize prediction drift in image classification models, Vertex AI Team would recommend the following:
• Augmenting your data such that you have a more diverse set of samples. This set should match your business needs, and has meaningful transformations given your context. Please refer to [2] for more information about data augmentation.
• Utilizing Vertex Explainable AI to identify the features which are contributing the most to your model's classification decisions. This would help you to augment your data in a more educated manner. Please refer to [3] for more information about Vertex Explainable AI.
[1] https://cloud.google.com/vertex-ai/docs/model-monitoring/overview
[2] https://www.tensorflow.org/tutorials/images/data_augmentation
[3] https://cloud.google.com/vertex-ai/docs/explainable-ai/overview