Thanks! One of the benefits of Lobe is that users who build models from scratch can publish and share to use in other documents, like a community model zoo. We do this for the current architectures internally, but the goal is for the community to help keep up with the firehose state-of-the-art in ML.
Something really interesting we have discussed for a future feature is being able to train a model using the data of which architectures end up working best for different data types so that Lobe can use AutoML to suggest better templates starting out, or on the fly while you are building the model.
That sounds amazing! I love the idea of integrating ML inside the user experience itself to create a feedback loop that looks for templates that would suit the user, based on the user's previous models and the type of work they are doing. Would you also layer in some type of semantic meta-tagging and specification engine which allows the user to pull in tags and keywords from other software, to help train their own personalized decision support, beyond templates?it would be awesome to connect this to something like https://www.IRIS.AI and get template recommendations alongside recommendations for additional research papers to review on the topic. I couldn't believe how effective this type of integrator could be until I imported my 7+ years of Evernote notes and tags, into my Devonthink document manager. The recommendations I get from both the index of pdfs and my own personal tagged notes create a sum greater then its parts. Your platform looks like an amazing tool to add to that mix.
Oh gotcha! Didn't consider that you'd allow users to write their own models too.
Coming to community model zoo, would it be free access to any model, and pay for the training and disk usage (floydhub like)? Or you'd go the quantopian route?
We are focusing on free to access any model explicitly shared by the user and pay for training/deploy resources as a service, but might consider mixing in a paid route for users to monetize their unique trained models.
Something really interesting we have discussed for a future feature is being able to train a model using the data of which architectures end up working best for different data types so that Lobe can use AutoML to suggest better templates starting out, or on the fly while you are building the model.