This article describes the key concept of the ModelOp Center Governance Inventory.
Table of Contents
Table of Contents |
---|
...
|
...
|
Use Cases and Model Implementations
...
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 that contains: (1) model output (2) labels/ground truth for each model output | 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 •Production (Comparator) Data that contains: (1) model output (2) labels/ground truth for each model output | UI, 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 /or Baseline Datathat contains all applicable model features in the training or baseline data set •Comparator Data CLI: would trigger the API that contains the exact same model features listed in the training or baseline data set | UI, 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, •Production (Comparator) Data that contains: (1) model output (2) labels/ground truth for each model output (3) the protected class for each model record | 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 LLM Tests on Model Output Data (e.g. Sentiment Analysis, Top Words by Parts of Speech, PII Leakage Detection, Toxicity, Gibberish Detection) | •Production (Comparator) Data that contains the model output (e.g. responses from a chatbot) | UI, 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> | |
Run LLM Tests using Known Questions & Answers (e.g. Similarity Analysis, Cross-LLM Accuracy, Cross-LLM Fact Checking) | •Production (Known_Questions) Data that contains: (a) known set of questions (b) the model output from a known set of questions (c) human-reviewed answers to the questions •LLM (e.g. GPT-4o) for cross-LLM tests | UI, API, MLC | S3: •Known_Questions_Data: s3://<model_base>/KnownQuestionsData.csv HDFS: •Known_Questions_Data: hdfs://<model_base>/KnownQuestionsData.csv SQL: •Known_Questions_Data: Training: SELECT * FROM <read_only_Known_Questions_table> WHERE <conditions> | |
Run LLM Guardrails Validation | •Guardrail_Testing_Questions that contains: (a) known set of questions to validate guardrails efficacy (b) human-reviewed expected results (e.g. guardrails should filter the answers out or allow it through) | UI, API, MLC | S3: •Guardrail_Questions_Data: s3://<model_base>/GuardrailQuestionsData.csv HDFS: •Guardrail_Questions_Data: hdfs://<model_base>/GuardrailQuestionsData.csv SQL: •Guardrail_Questions_Data: Training: SELECT * FROM <read_only_Guardrail_Questions_table> WHERE <conditions> | |
Run a Training Job | •Training Data | MLC, UI, API, MLC | 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> |
...
Purpose: provides a list of all assets that are required to run a given metrics model (test/monitor).
Usage: primarily used by the ModelOp Center UI to indicate to the user which assets are required when adding a monitor.
Location: this file should be included in the model’s git repository.
Import: when importing the model from git, ModelOp Center will recognize this file and automatically use it to populate the “Add a Monitor” wizard “assets” screen.
Structure: needs to be valid json that follows the ModelOp Center “required assets” structure. Please see this article on the structure of the ModelOp Center “required assets” structure.
Next Article: Inventory Metadata >