Hey folks, I’m scratching my head over this Vertex AI Agent Builder thing. I set up a Recommendations model for Media content and it trained fine. But here’s the weird part: I deleted a document from the data store (checked it’s gone for sure), but the model still shows it in predictions! What gives?
I can’t find anything in the docs about this. Do I need to retrain the model every time I update the data store? Is there some hidden index I need to update? I tried purging the data store, but no luck.
I expected the model to use live data from the store. If something’s deleted or changed, it shouldn’t show up anymore, right? But it keeps popping up like a bad penny.
Anyone run into this before? Any tips on how to get the model to sync up with the current data store contents? I’m totally stumped here!
Man, I feel your frustration! I’ve been wrestling with this exact problem in my recommendation system for a music streaming app. It’s a real headache.
Here’s what I figured out: Vertex AI’s model is like a stubborn old database - it doesn’t update on its own. I ended up setting up a nightly retraining job. It’s not perfect, but it keeps things reasonably fresh.
One trick that helped: I added a quick check in my app code. Before displaying a recommendation, it pings the datastore to make sure that item still exists. Bit of extra work, but it catches those ghost recommendations.
Have you looked into using a separate, lightweight index for real-time updates? I’m experimenting with that now. Keeps the heavy lifting on the AI side, but gives me more control over what actually gets shown.
Hopefully Google’s working on a better solution. For now, we’re stuck with these workarounds. Hang in there!
I’ve actually encountered a similar issue with Vertex AI Agent Builder’s Recommendation model. From my experience, the model doesn’t automatically sync with the data store in real-time. It’s more of a snapshot of the data at the time of training.
What worked for me was implementing a periodic retraining schedule. I set up an automated process to retrain the model weekly, which helped keep predictions more up-to-date with the current data store contents. It’s not ideal, but it’s a workable solution.
Another approach I found helpful was to implement a post-processing step. After getting predictions, I cross-reference them with the current data store to filter out any outdated results. It adds a bit of overhead, but it ensures you’re not showing recommendations for deleted items.
Have you considered reaching out to Google support? They might have some insights on best practices or upcoming features to address this limitation. It’s definitely a pain point that I hope they improve in future updates.
yo, i’ve been there too. it’s a real pain. the model’s like a snapshot, not real-time. what i do is retrain it often, like every few days. also, i double-check recommendations against my database before showing em. not perfect, but it works ok. maybe google will fix this someday? fingers crossed!
I’ve dealt with this issue in my projects as well. The Vertex AI Agent Builder’s Recommendation model doesn’t dynamically update with data store changes. It’s essentially a static snapshot from when you trained it.
To address this, I implemented a two-pronged approach. First, I set up automated retraining on a regular schedule (weekly in my case) to keep the model relatively current. Second, I added a validation step in my application logic. Before displaying recommendations, I check if the items still exist in the data store.
While not perfect, this method has significantly reduced outdated recommendations. It does add some processing overhead, but the trade-off in accuracy is worth it. You might want to experiment with retraining frequency based on how often your data changes.
Have you considered using a caching layer between your model and data store? This could potentially offer a more real-time solution without constant retraining.