CUDA
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I enabled autoscaling to handle traffic spikes.Instead of improving performance, latency increased.Cold starts seem frequent.This feels counterproductive.
We started with a few simple record-triggered Flows, and they worked well initially. Over time, more conditions, paths, and updates were added to handle new requirements. Now debugging and updating these Flows feels risky and time-consuming. I’m trying to understand ...
I fine-tuned a pretrained Transformer on a small custom dataset.Training finishes without errors.But the generated outputs look random and off-topic.It feels like the model forgot everything.
Training loss decreases smoothly.Validation loss fluctuates.Regularization is enabled.Still, generalization is poor.
My model uses both image and text inputs.It works well when both are provided.If one modality is missing, outputs become random or broken.Real-world data is often incomplete.
I’ve already removed SOQL queries and DML operations from loops in my Apex code. Despite that, I’m still hitting limits in real production scenarios with large datasets. The logic itself isn’t very complex, but the limits seem easy to hit. ...