AI governance use case
To drive responsible, transparent, and explainable AI workflows, your enterprise needs an integrated system for tracking, monitoring, and retraining AI models. Cloud Pak for Data as a Service provides the processes and technologies to enable your enterprise to monitor, maintain, automate, and govern machine learning and AI models in production.
Watch this video to see the use case for implementing a AI governance solution.
This video provides a visual method to learn the concepts and tasks in this documentation.
Challenges
You can solve the following challenges for your enterprise by implementing a AI governance use case:
- Ensuring model governance and compliance
- Organizations need to track and document the detailed history of models to ensure compliance and to provide visibility to all stakeholders.
- Managing risk and ensuring responsible AI
- Organizations need to monitor models in production to ensure that the models are valid and accurate, and that they are not introducing bias or drifting away from the intended goals.
- Operationalizing the model lifecycle
- Organizations need to implement repeatable processes to efficiently retrain and deploy models to production environments.
Example: Golden Bank's challenges
Follow the story of Golden Bank as it implements an AI governance process to make sure that its new online application process is compliant and explainable. Business analysts at Golden Bank need to review model information to ensure compliance,
certify model progress from development to production, and generate reports to share or archive.
Process
To implement AI governance for your enterprise, your organization can follow this process:
The watsonx.ai Studio, watsonx.ai Runtime, Watson OpenScale, and IBM Knowledge Catalog services in Cloud Pak for Data as a Service provide the tools and processes that your organization needs to implement an AI governance solution.
1. Track models
Your team can track your machine-learning models from request to production and evaluate whether the models comply with your organization's regulations and requirements.
| What you can use | What you can do | Best to use when |
|---|---|---|
| Factsheets | In the model inventory in a catalog in IBM Knowledge Catalog, create a use case for a new model. View lifecycle status for all of the registered assets and drill down to detailed factsheets for models or deployments that are registered to the model use case. View general model details, training information and metrics, and input and output schema. View general deployment details, evaluation details, quality metrics, fairness details, and drift details. |
You need to request a new model from your data science team. You want to make sure that your model is compliant and performing as expected. You want to determine whether you need to update a model based on tracking data. You want to run reports on a model to share or preserve details. |
Example: Golden Bank's model tracking
Business analysts at Golden Bank request a "Mortgage Approval Model". They can then track the model through all stages of the AI lifecycle as data scientists build and train the model and ModelOps engineers deploy and evaluate it. Factsheets document details about the model history and generate metrics that show its performance.
2. Monitor deployed models
After models are deployed, it is important to govern and monitor them to make sure that they are explainable and transparent. Data scientists must be able to explain how the models arrive at certain predictions so that they can determine whether the predictions have any implicit or explicit bias. In addition, it's a best practice to watch for model performance and data consistency issues during the lifecycle of the model.
| What you can use | What you can do | Best to use when |
|---|---|---|
| Watson OpenScale | Monitor model fairness issues across multiple features. Monitor model performance and data consistency over time. Explain how the model arrived at certain predictions with weighted factors. Maintain and report on model governance and lifecycle across your organization. |
You have features that are protected or that might contribute to prediction fairness. You want to trace model performance and data consistencies over time. You want to know why the model gives certain predictions. |
Example: Golden Bank's model monitoring
Data scientists at Golden Bank use Watson OpenScale to monitor the deployed "Mortgage Approval Model" to ensure that it is accurate and treating all Golden Bank mortgage applicants fairly. They run a notebook to set up monitors for the model and then tweak the configuration by using the Watson OpenScale user interface. Using metrics from the Watson OpenScale quality monitor and fairness monitor, the data scientists determine how well the model predicts outcomes and if it produces any biased outcomes. They also get insights for how the model comes to decisions so that the decisions can be explained to the mortgage applicants.
3. Automate the AI lifecycle
Your team can automate and simplify the MLOps and AI lifecycle with Orchestration Pipelines.
| What you can use | What you can do | Best to use when |
|---|---|---|
| Orchestration Pipelines | Use pipelines to create repeatable and scheduled flows that automate machine learning pipelines, from data ingestion to model training, testing, and deployment. | You want to automate some or all of the steps in an MLOps flow. |
Example: Golden Bank's automated ML lifecycle
The data scientists at Golden Bank can use pipelines to automate their complete AI governance lifecycle and processes to simplify the model retraining process.
Tutorials for AI governance
| Tutorial | Description | Expertise for tutorial |
|---|---|---|
| Build and deploy a model tutorial | Train a model, promote it to a deployment space, and deploy the model. | Run a notebook. |
| Test and validate a model tutorial | Evaluate a model for accuracy, fairness, and explainability. | Run a notebook, and view results in the user interface. |
Learn more
Parent topic: Use cases