- What does the future road map look like?
- Are you planning on adding more algorithms ?
- Any plans to bring in dataset and dataframe handling capabilities such as in numpy/pandas etc?
- What other interop features with other languages/platforms are planned?
- Any plans for AutoML features?
- Yep.
- There are various efforts on the JVM to build multidimensional arrays, we're talking to many of them to try and figure out a strategy for the whole platform. Ditto for dataframes, though Apache Arrow looks like a good baseline.
- We're not looking at other languages outside of the JVM at the moment, but we're continuing to contribute to Tensorflow Java and ONNX Runtime to improve their Java support. We could look at pytorch inference support based on their Java API, but that overlaps pretty well with the things that ONNX Runtime supports. Do you have any suggestions?
- Not beyond hyperparameter tuning.
Many models are deployed as restful endpoints, so a quick and easy ways to deploy models as services with containers or serverless providers will be very useful - although admittedly, you might not want that in the core project, could be a good sidecar project to this. Given your focus on model provenance, extending that beyond to model deployment and life cycle management tools such as MLFlow could also be very useful