I would like to train the model using Spark ML Lib, but then I can export the model in a platform-agnostic format. Essentially, I want to separate how models are created and consumed.
My reason is that you want to untie, so that I can deploy the model to other projects. For instance:.
- Use the model to make forecasts in a separate, stand-alone program that is independent of Spark for evaluation.
- Use the model with existing projects, such as OpenScoring, and provide APIs that can use the model.
- Upload your existing model back to Spark to predict high throughput.
Has anyone done something similar with the Spark ML Lib?
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