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{
"id": "6d4b571a-80ca-41ef-bc67-b158f4352ad8",
"name": "Model 1",
"revision": 3,
"format": {
"name": "PMML",
"version": "4.3"
},
"algorithm": "Neural Network",
"tags": [
"Anomaly detection",
"Banking"
],
"dependency", "",
"creator": "John Doe",
"description": "This is a predictive model, refer to {input} and {output} for detailed format of each field, such as value range of a field, as well as possible predictions the model will gave. You may also refer to the example data here.",
"input": {
"fields": [
{
"name": "Account ID",
"opType": "categorical",
"dataType": "string",
"taxonomy": "ID",
"example": "account abc-001",
"allowMissing": false,
"description": "unique value"
},
{
"name": "Account Balance",
"opType": "continuous",
"dataType": "double",
"taxonomy": "currency",
"example": "1,378,560.00",
"allowMissing": true,
"description": "Minimum: 0, Maximum: 999,999,999.00"
},
],
"ref": "http://dmg.org/pmml/v4-3/pmml-4-3.xsd"
}
"output": {
"fields": [
{
"name": "Churn",
"opType": "continuous",
"dataType": "string",
"taxonomy": "ID",
"example": "0.67",
"allowMissing": false,
"description": "the possibility of the account stop doing business with a company over 6 months"
}
],
"ref": "http://dmg.org/pmml/v4-3/pmml-4-3.xsd"
}
"performance": {
"metric": "accuracy",
"value": 0.85
},
"rating": 5,
"url": "uri://link_to_the_model"
} |
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Authors
Potential Future Enhancement
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- Tableau version of OBAIC https://tableau.github.io/analytics-extensions-api/docs/ae_example_tabpy.html
- Qlik version of OBAIC: https://github.com/qlik-oss/server-side-extension
- Delta Sharing: https://github.com/delta-io/delta-sharing/blob/main/PROTOCOL.md#delta-sharing-protocol
Authors
Decision to be made
- Data file type: What type of data we are supporting: e.g. for Delta needs to be parquet, RDBMS? Can modify the Jeffrey init cut below to support multiple data types, depending on the use case.
- Inference: Pass by value should be good enough if it's only for predicting
- Train: not immediate, maybe later in Phase 2
- Metadata structure, what kind of JSON schema do we need
- Do we only support a specific model type (ONNX) or arbitrary number of framework
- Decouple model (asking the model to predict and train) and data (listing, upload, download)
- Finalize Logo
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How can the data be accessed mechanically, for training?
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Original content from Jeffrey. To be integrated with the main content
This is a short doc illustrating a sample skeleton OBAIC protocol. This proposal envisions a data-centric workflow:
- BI vendor has some data on which predictive analytics would be valuable.
- BI vendor requests AI vendor (through OBAIC) to train/prepare a model that accepts features of a certain type (numeric, categorical, text, etc.)
- BI vendor gives AI vendor a token to allow access to the training data with the above features. A SQL statement is a natural way to specify how to retrieve data from the datastore.
- When model is trained, BI vendor can see the results of training (e.g., accuracy).
- AI vendor provides predictions on data shared by BI vendor, again using an access token.
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Train a New Model
function TrainModel(inputs, outputs, modelOptions, dataConfig) -> UUID |
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