I'm wondering what ML model formats you use and what is specification for inputs\results? Do you try to use some common format or it is fine for you to use a proprietary one?
I'm building https://DVC.org - Git for data. We think about how to save and version ML models properly (through GitFlow). One of the biggest challenges - there are not common formats for ML models, inputs and scorings formats.
I'd appreciate if you could share your opinion about models format generalization.
Good to meet you -- I'm a big fan of DVC. In our implementation, we've taken the approach of conforming to the standards (typically open-source) set by the frameworks for serialization (for example https://www.tensorflow.org/guide/saved_model). In our API design, it was important to integrate at the framework level (e.g. a tf.keras.models.Model object) for our client libraries. If you're using one of the widely available frameworks that we support, this results in a simple API where the serialization / deserialization is more of an implementation detail. If you're using a custom or rarer ML framework with an unstandardized serialization format, an open-source approach might work better.
Hope that was helpful!
It would be great to come up with a common format for all these pieces. So, many levels of ML stack can use it.
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