Overview
ModelOp Center is designed to be agnostic to the type of model, the platform on which the model runs, and the data platforms with which the model interacts during various portions of the model lifecycle. Central to the execution of a model are the model-specific assets. Types of Model Assets that may be used during the Model Life Cycle Include:
Trained model artifacts
Requirements (Dependencies) lists
Data assets
Configuration files
Asset Types
Data Assets
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
While assets can be used in a variety of activities throughout a model life cycle, below is a summary of typical activities where a User or an MLC may use a Data Asset:
Action | Data Required | Available via: | MOC Asset Examples (note: these examples are not the only ways to define assets) |
Add an Asset (to a Business 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) | Test Data | CLI, UI, API, MLC | S3: •Test_Data: s3://<model_base>/TestData.csv HDFS: •Test_Data: hdfs://<model_base>/TestData.csv SQL Asset: •Test_Data: SELECT * FROM <read_only_Test_Data_table> WHERE <conditions> |
Run a Performance Metrics Job | •Comparator Data | CLI: would trigger the API API, MLC | S3: •Compare_Data: s3://<model_base>/ComparatorData.csv HDFS: •Compare_Data: hdfs://<model_base>/ComparatorData.csv SQL: •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 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> |
Other Assets
Trained Model Artifacts
ModelOp Center supports Trained Model Artifacts stored in S3 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.
Schemas
ModelOp Center runtime supports data schemas which helps to define 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.
Requirements
ModelOp runtime comes with basic pre-installed libraries for different model and languages. If the model requires additional libraries to be installed by the ModelOp Runtime, it supports defining model library requirements in requirements.txt file. The ModelOp runtime will check the requirements file before running the model and install the missing libraries. When uploading the requirements file, please select the asset role of “Requirements”.
Adding Model Assets
During Model Import
Please see Register a Model to get details about adding assets during model import.
Via ModelOp UI
Go to the Model Details page by clicking on the model from the Models page
View the existing Assets by clicking on Assets tab:
Click on “Add Assets” , which presents multiple options for adding assets:
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:
For Local File Upload:
Click on the “File Upload”
Select the file from your local computer’s file system and hit “Open”
The file will be uploaded as a new asset on the model
To Add Asset by Reference:
Click “URL” from the Add Asset menu
Provide the URL to the file.
Select the type of asset: S3, Google Cloud Bucket, Azure Blob Store, HDFS. Note that your system administrator may have already pre-configured the type of asset store for your group
Fill in the other pertinent storage information
Click “Save” to save the asset
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
Click “REST” from the Add Asset menu
Fill in the Asset Name, Asset Role, Request Method, Target URL, Query Params to be sent in the request.
If the REST asset does NOT use standard HAL paging, select the “Requires Custom Paging” option and fill in the details
Click “Save”
The REST asset will be uploaded as a new asset on the model
To Add a SQL Asset
Click “SQL” from the Add Asset menu
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.
Next Article: Model Life Cycle Management: Overview >