The Many Ways to Deploy a Model | Outerbounds

Top considerations when choosing foundation models

Accuracy

Cost

Latency

Privacy

Top challenges when deploying production AI

Serving cost

Evaluation

Infra reliability

Model quality

We generally lean towards picking more advanced commercial LLMs to quickly validate our ideas and obtain early feedback from users. Although they may be expensive, the general idea is that if problems can't be adequately solved with state-of-the-art foundational models like GPT-4, then more often than not, those problems may not be addressable usin... See more

Hi everyone! How do you guys go about choosing the granularity of your ML response ?. For instance, let us say you have been tasked with predicting the purchase probability for an item and this is how your merch hierarchy looks -

1) department

2) category

3) sub category

4) item

The trade off here is between granularity and response sparsity ie if you ... See more