yo alex, i’ve faced that too. the model doesn’t sync realtime with the data store; you need to retrain it after updates. sux, huh? maybe check google’s support for a workaround.
yea, i feel ya. vertex AI can be a pain sometimes. the model’s like a snapshot, so it doesn’t update in real-time with your data store. kinda defeats the purpose, right? you gotta retrain it manually after big changes. maybe look into automating that process if u can. good luck!
I’ve grappled with this issue in Vertex AI Agent Builder as well. The Recommendation model operates on a snapshot principle, not syncing real-time with the data store. This design choice leads to the behavior you’re experiencing.
To address this, you’ll need to retrain the model after significant data changes. It’s not ideal, but it’s the current limitation. There’s no built-in re-indexing feature, unfortunately.
For a workaround, consider implementing an automated retraining pipeline on a regular schedule. This could help keep your model more up-to-date without constant manual intervention.
If real-time accuracy is crucial, you might need to explore alternative recommendation systems or implement a hybrid approach, using the model for bulk recommendations and a separate system for recent changes.
I’ve encountered similar issues with Vertex AI Agent Builder. The Recommendation model doesn’t automatically update when you modify the data store. It’s a limitation of the current implementation.
To address this, you’ll need to retrain the model after making significant changes to your dataset. This process can be time-consuming, especially for large datasets. Unfortunately, there’s no quick fix or re-indexing option available at the moment.
For your use case, consider implementing a scheduled retraining pipeline. This way, you can ensure your model stays up-to-date with your latest data without manual intervention. It’s not ideal, but it’s the best workaround I’ve found so far.
If real-time updates are crucial for your application, you might want to explore alternative recommendation systems that offer more dynamic data handling.
As someone who’s worked extensively with Vertex AI Agent Builder, I can shed some light on your issue. The Recommendation model doesn’t dynamically update with changes to the data store - it’s a static snapshot from when it was trained.
This behavior is by design, albeit frustrating. The model is essentially decoupled from the live data after training. To reflect changes, you need to retrain the model, which can be resource-intensive and time-consuming.
In my experience, a good workaround is to implement a regular retraining schedule, maybe weekly or bi-weekly depending on how often your data changes. You could automate this process using Cloud Functions or Cloud Scheduler.
For more immediate updates, you might consider a hybrid approach: use the Recommendation model for the bulk of suggestions, but implement a separate system to filter out recently deleted items or add new ones. It’s not perfect, but it can help bridge the gap between retraining cycles.
Hope this helps provide some clarity on the situation!