3 ms·

Neat idea. 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

Neat idea. 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 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.

yoavz 6y ago
Hey Dmitry! 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 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!
- dmpetrov 6y ago
  Relying on the frameworks formats seems like a great approach. However, there are a few more open questions related to model governance. Versions of frameworks, input data specification, output data/scores specification. I'd expect all of these pieces to be part of ML model description (this is even more important for model serving, then storing/versioning). It would be great to come up with a common format for all these pieces. So, many levels of ML stack can use it.

3 ms·

Neat idea. 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

by dmpetrov 6y ago

yoavz 6y ago
Hey Dmitry! 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 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!
- dmpetrov 6y ago
  Relying on the frameworks formats seems like a great approach. However, there are a few more open questions related to model governance. Versions of frameworks, input data specification, output data/scores specification. I'd expect all of these pieces to be part of ML model description (this is even more important for model serving, then storing/versioning). It would be great to come up with a common format for all these pieces. So, many levels of ML stack can use it.