ML Service Interactions


ML Backend Type Data Input Format Model Creation Query Format
Azure ML service
Big ML service
    • CSV
    • ARFF
    • <= 64GB, can be gzipped (4TB if the file is stored on Amazon S3)
    • can create sources from remote locations: supported protocols include azure, https, odata, s3, and dropbox
    • Creating a source is a service, as is updating a source, so this can be easily automated.
    • Service call: send dataset's id
    • creation is async: you must query to determine if it's finished
    • Can also use a service call to update the model, but that only updates the model's metadata, it does not actually retrain it
    • you can easily create ensemble models, which is pretty cool
    • async batch predictions are also available
    • send model id and input data as JSON, plus username and api key
    • get back prediction plus a bunch of metadata, including a confidence score, as JSON
      • can statically refer to this prediction later by a unique ID
Google Predictions service
  • Upload a CSV to Google Cloud Storage
  • Can call a service endpoint to update the data set of a model (I don't think this auto-retrains the model, though)


    • you can batch requests, but that does not seem to be an async operation
    • send model and project ids, along with a list of input features (JSON structure mostly consisting of a JSON list)
    • get back JSON with output value. If task was classification, also get back a list of all class labels with their associated probabilities.
      • also gets back a url to re-request the same prediction again


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