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ModelOp Center “Assets” support various data technologies, including:

  • AWS S3 or S3-Compliant S3 Files

  • Azure Blob Storage Files

  • GCP Storage Buckets

  • HDFS Files

  • SQL data sources

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Action

Data Required

Available via:

MOC Asset Examples [ASSET_ROLE : EXAMPLE}(note: these examples are not the only ways to define assets)

Add an Asset (to a StoredModelBusiness Use Case, Model, or Snapshot)

All types

CLI: s3 & embedded assets only

UI: all

API: all

Applicable to S3, Azure Blob, GCP Storage Buckets, HDFS, and SQL Asset

Run a Metrics Job

(e.g. “Back Test” using labeled data)

•Evaluation Test Data

CLI, UI, API, MLC

S3:

Test_Data: s3://<model_base>/EvaluationDataTestData.csv

HDFS:

Test_Data: hdfs://<model_base>/EvaluationDataTestData.csv

SQL Asset:

Test_Data: SELECT * FROM <read_only_Test_EvalData_table> WHERE <conditions>

Run a Data Drift Performance Metrics Job

•Training/Baseline Data

•Comparator Data

CLI: would trigger the API

API, MLC

S3:Training_Data: s3://<model_base>/TrainingData.csv

Compare_Data: s3://<model_base>/ComparatorData.csv

HDFS:

Training_Data: hdfs://<model_base>/TrainingData.csv•Compare_Data: hdfs://<model_base>/ComparatorData.csv

SQL:

Training_Data: SELECT * FROM <read_only_Training_table> WHERE <conditions>•Compare_Data: Training: SELECT * FROM <read_only_Comparator_table> WHERE <conditions> … may need to have tag to specify input vs. output comparator data

Run a Model Concept Drift Distribution Comparison (e.g. Drift) Job

•Training/Baseline Data

•Comparator Data

CLI: would trigger the API

API, MLC

S3:

Training_Data: s3://<model_base>/TrainingData.csv

Compare_Data: s3://<model_base>/ComparatorData.csv

HDFS:

Training_Data: hdfs://<model_base>/TrainingData.csv

Compare_Data: hdfs://<model_base>/ComparatorData.csv

SQL:

Training_Data: SELECT * FROM <read_only_Training_table> WHERE <conditions>

Compare_Data: Training: SELECT * FROM <read_only_Comparator_table> WHERE <conditions> … may need to have tag to specify input vs. output comparator data

Run a Bias Detection Job

Evaluation Data

CLI, UI, API, MLC

S3:

Test_Data: s3://<model_base>/EvaluationData.csv

HDFS:

Test_Data: hdfs://<model_base>/EvaluationData.csv

SQL Asset:

Test_Data: SELECT * FROM <read_only_Eval_table> WHERE <conditions>

Run a Training Job

•Training Data

MLC, UI, API

S3:

Training_Data: s3://<model_base>/TrainingData.csv

HDFS:

Training_Data: hdfs://<model_base>/TrainingData.csv

SQL Asset:

Training_Data: SELECT * FROM <read_only_Training_table> WHERE <conditions>

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ModelOp Center supports Trained Model Artifacts stored in S3 bucket or HDFS (does not support as SQL Asset)buckets, Azure Blob Store, GCP Storage Buckets, or Artifactory. When adding asset that is used as Trained Model Artifact, please select the asset role to be “Weights File” or “Model Binary File” depending on the use case.

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ModelOp Center runtime supports data schemas which helps to enforce specif type of on different fieldsdefine the data inputs/outputs for testing, monitoring, scoring, and governance. The schema can be used for input data and/or output data. When uploading a schema, select “Model Schema” asset role.

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Please see Register a Model to get details about adding assets during model import.

Via ModelOp UI

  1. Go to the Model Details page by clicking on the model from the Models page

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  2. View the existing Assets by clicking on Assets tab:

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  2. Click on “Add Assets” , which

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  1. presents multiple options for adding assets:

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  2. The user is presented with multiple options for adding assets: as a local file upload, by providing a reference (url) to an existing location, as a REST asset, or by providing as SQL query:

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Upload a Local File

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:

  1. Click on the

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  1. “File Upload”

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Click on “Upload file”:

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  1. Select the file from your local

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Select to Upload the file:

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  1. computer’s file system and hit “Open”

  2. The file will be uploaded as a new asset on the model

To Add an Asset by Reference:

  1. Click “URL” from the Add Asset menu

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  2. Provide the URL to the file.

  3. Select the type of asset: S3,

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  1. Google Cloud Bucket, Azure Blob Store

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Click Add Asset

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  1. , HDFS. Note that your system administrator may have already pre-configured the type of asset store for your group

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  2. Fill in the other pertinent storage information

  3. Click “Save” to save the asset

  4. The file will be uploaded as a new asset on the model

To Add a REST Asset

REST assets are those data sets or other assets that can be retrieved via standard REST API calls

  1. Click “REST” from the Add Asset menu

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  2. Fill in the Asset Name, Asset Role, Request Method, Target URL, Query Params to be sent in the request.

    1. If the REST asset does NOT use standard HAL paging, select the “Requires Custom Paging” option and fill in the details

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  3. Click “Save”

  4. The REST asset will be uploaded as a new asset on the model

To Add a SQL Asset

  1. Click “SQL” from the Add Asset menu

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    Please see Integrate with SQL Databases to understand how ModelOp Center integrates with SQL databases. Once you get familiar with how ModelOp Center handles SQL Assets, follow these steps to add SQL assets to a given model.

Via CLI

Please see the asset command for adding assets using the CLI.

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