The Many Ways to Deploy a Model | Outerbounds
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The Many Ways to Deploy a Model | Outerbounds
In general, I see LLMs to be used in two broad categories: data processing, which is more of a worker use-cases, where the latency isn't the biggest issue but rather quality, and in user-interactions, where latency is a big factor. I think for the faster case a faster fallback is necessary. Or you escalate upwards, you first rely on a smaller more ... See more
Discord - A New Way to Chat with Friends & Communities
MLEs are happy to delegate experiment tracking and execution work to ML experiment execution frameworks, such as Weights & Biases 3 , but prefer to choose subsequent experiments themselves. To be able to make informed choices of subsequent experiments to run, MLEs must maintain awareness of what they have tried and what they haven’t (Lg2 calls ... See more
Shreya Shankar • "We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning.
Answer.AI - What policy makers need to know about AI (and what goes wrong if they don’t)
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
1) department
2) category
3) sub category
4) item
The trade off here is between granularity and response sparsity ie if you ... See more
Discord - A New Way to Chat with Friends & Communities
Nature of the problem (Regression, Classification, Clustering, etc.) – Number of data records available for training – Decision support or cognitive use case? – Need for explain-ability