This looks pretty great, though the SaaS model is an absolute non-starter for my own usage unfortunately. We've been pretty prolific users of Explosion AI's (Makers of SpaCy) Prodigy [1] and actually the interfaces look very similar. What would you say the core differences are between Humanloop and Prodigy?
Thanks! Prodigy is a good tool and we definitely were inspired by some of their UX decisions. Reducing each decision to a small atomic unit and avoiding context switching makes a lot of sense.
Our starting place is similar to Prodigy in that we also see active learning as a key piece of the puzzle but we think to make active learning work reliably really does need taking into account parameter uncertainty. As far as I know Prodigy doesn't do this. We are also working to make our active learning work at the level of batches and be cost-aware. Often the most valuable examples to label for the model are the most time consuming for humans and we work to trade this off.
A few other differences are that we do offer a cloud hosted solution so getting set up is much faster and it's more natural for us to be able to accomodate team annotation and quality assurance. By providing a hosted model we also give you the option of deploying features very quickly and continuing to improve them post deployment.
I'd be curious to know the barriers that Saas introduces for you?
Good to know! I work with a lot of highly classified information. Much to my teams frustration, the notion that any of which will be cloud hosted on GCP or AWS was laughable until somewhat recently. It will probably be years until we, and I imagine many institutions like ours, can take advantage of something like this. I appreciate the effort with the VPS / private cloud hosting option, but without an on-prem deployment option, this wouldn't even make it passed initial discussions.
Comments
This looks pretty great, though the SaaS model is an absolute non-starter for my own usage unfortunately. We've been pretty prolific users of Explosion AI's (Makers of SpaCy) Prodigy [1] and actually the interfaces look very similar. What would you say the core differences are between Humanloop and Prodigy?
1: https://prodi.gy/
Thanks! Prodigy is a good tool and we definitely were inspired by some of their UX decisions. Reducing each decision to a small atomic unit and avoiding context switching makes a lot of sense.
Our starting place is similar to Prodigy in that we also see active learning as a key piece of the puzzle but we think to make active learning work reliably really does need taking into account parameter uncertainty. As far as I know Prodigy doesn't do this. We are also working to make our active learning work at the level of batches and be cost-aware. Often the most valuable examples to label for the model are the most time consuming for humans and we work to trade this off.
A few other differences are that we do offer a cloud hosted solution so getting set up is much faster and it's more natural for us to be able to accomodate team annotation and quality assurance. By providing a hosted model we also give you the option of deploying features very quickly and continuing to improve them post deployment.
I'd be curious to know the barriers that Saas introduces for you?
Good to know! I work with a lot of highly classified information. Much to my teams frustration, the notion that any of which will be cloud hosted on GCP or AWS was laughable until somewhat recently. It will probably be years until we, and I imagine many institutions like ours, can take advantage of something like this. I appreciate the effort with the VPS / private cloud hosting option, but without an on-prem deployment option, this wouldn't even make it passed initial discussions.