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Congratulations on launch! ML tools/services/platforms are really hard to build. You need to juggle not only frontend/backend frameworks but also make them work

Congratulations on launch! ML tools/services/platforms are really hard to build. You need to juggle not only frontend/backend frameworks but also make them work with ML frameworks. There are so many corner-cases that can make the whole app crash. How does it differ from open-source AutoML frameworks like https://github.com/mljar/mljar-supervised https://github.com/mljar/mljar-supervised or drag-and-drop tools like Azure ML Studio? Is the no-writing the code a killer feature here? Do you have financial data enhancement feature? Do you plan such feature?

avais 6y ago
yeaaah - we've experienced corner cases problem a lot. We're covering the functionality with unit tests and based on mock data, but still validating many things manually and sometimes need to do hot-fixes :) Though, the good side of this is that the users don't need to this testing on their end once building on top of datrics, so we (hopefully) save some time here.
avais 6y ago
Re killer feature: I believe it's being end-to-end, provide experience for "citizen x", domain experts, rather than only ML engineers: so it's no-code data cleansing, transformation + ML layer on top of this + productionalization, and being cloud-agnostic. We found that the problem that we're trying to solve is bigger than just giving the possibility to use ML instruments for non-developers. There are a lot of citizen data scientists who want to automate their spreadsheet work, make inference easy once the model is created without too much efforts to change processing pipeline, etc. That's why we don't compete with Azure ML Studio as they more developers-focused and with AutoML tools as ML is only a feature of platform. In addition, we've designed and implemented own pipeline scheduling algorithm and APIs out-of-the-box, so ready pipelines or models could be easily embedded into business process or used outside of the platform.
avais 6y ago
Right now there is a possibility to integrate third-party data with custom code + API calls, but I believe we need to work on this extensively to provide easier way for this. So happy to learn if you have some specific sources in mind?

avais 6y ago

yeaaah - we've experienced corner cases problem a lot. We're covering the functionality with unit tests and based on mock data, but still validating many things manually and sometimes need to do hot-fixes :) Though, the good side of this is that the users don't need to this testing on their end once building on top of datrics, so we (hopefully) save some time here.

avais 6y ago

Re killer feature: I believe it's being end-to-end, provide experience for "citizen x", domain experts, rather than only ML engineers: so it's no-code data cleansing, transformation + ML layer on top of this + productionalization, and being cloud-agnostic. We found that the problem that we're trying to solve is bigger than just giving the possibility to use ML instruments for non-developers. There are a lot of citizen data scientists who want to automate their spreadsheet work, make inference easy once the model is created without too much efforts to change processing pipeline, etc. That's why we don't compete with Azure ML Studio as they more developers-focused and with AutoML tools as ML is only a feature of platform. In addition, we've designed and implemented own pipeline scheduling algorithm and APIs out-of-the-box, so ready pipelines or models could be easily embedded into business process or used outside of the platform.

avais 6y ago

Right now there is a possibility to integrate third-party data with custom code + API calls, but I believe we need to work on this extensively to provide easier way for this. So happy to learn if you have some specific sources in mind?