Track a machine learning model from request to production by gathering metadata and facts about the model lifecycle using AI Factsheets. Use the detailed information in the factsheets to keep stakeholders informed and to meet your governance and
compliance goals. Factsheets can be shared, archived, or printed as report.
Managing governance with AI Factsheets
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Before you develop and AI solution you must first define the business use case and then manage the development, testing, and deployment of the solution. You can manage and govern the flow of information by create a model use case, that defines
the goals of the model. When the model is approved and development starts, track the assets in the use case, capturing all relevant data with AI Factsheets. View at a glance which models are in production and which need development or validation.
Use the governance features to establish processes to manage the communication flow from data scientists to ModelOps administrators.
Note: Only the models that you add to use cases are tracked with AI Factsheets. You can control which models to track for an organization without tracking samples and other models that are not significant to the
organization.
Defining use cases in a model inventory
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The model inventory is a view where you can define a use case to request a new model, then track the model and related assets through its lifecycle. A typical flow might go as follows:
A business user identifies a need for a machine learning model and creates a model use case to request a new model. The business owner assigns a name and states the basic parameters for the requested model.
When the request is saved, a model use case is created in the inventory. Initially, the use case is in the Awaiting development state because there are no assets to accompany the request.
When a data scientist creates a model for the business case, they track the model from the model details page of the project or space, and associate it with the model use case.
The model use case in the inventory can now be moved to an In progress state and stakeholders can review the assets for the use case, which now include the model.
As the model advances in the lifecycle, the model use case and the AI factsheet reflect all updates, including deployments and input data assets.
Validators and other stakeholders can review model use cases to ensure compliance with corporate protocols and to view and certify model progress from development to production.
Use cases and tutorials
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AI Factsheets is part of IBM's data fabric collection of tools and capabilities for managing and automating your data and AI lifecycle. For details on how data fabric can support your governance goals in practical ways, see Use cases.
For real-world use cases and tutorials for using AI Factsheets to orchestrate AI solutions, see:
AI governance use case provides context for how ModelOps can mesh with AI Governance to provide a comprehensive plan for tracking machine learning assets in your organization.
Use this interactive map to learn about the relationships between your tasks, the tools you need, the services that provide the tools, and where you use the tools.
Select any task, tool, service, or workspace
You'll learn what you need, how to get it, and where to use it.
Some tools perform the same tasks but have different features and levels of automation.
Jupyter notebook editor
Prepare data
Visualize data
Build models
Deploy assets
Create a notebook in which you run Python, R, or Scala code to prepare, visualize, and analyze data, or build a model.
AutoAI
Build models
Automatically analyze your tabular data and generate candidate model pipelines customized for your predictive modeling problem.
SPSS Modeler
Prepare data
Visualize data
Build models
Create a visual flow that uses modeling algorithms to prepare data and build and train a model, using a guided approach to machine learning that doesn’t require coding.
Decision Optimization
Build models
Visualize data
Deploy assets
Create and manage scenarios to find the best solution to your optimization problem by comparing different combinations of your model, data, and solutions.
Data Refinery
Prepare data
Visualize data
Create a flow of ordered operations to cleanse and shape data. Visualize data to identify problems and discover insights.
Orchestration Pipelines
Prepare data
Build models
Deploy assets
Automate the model lifecycle, including preparing data, training models, and creating deployments.
RStudio
Prepare data
Build models
Deploy assets
Work with R notebooks and scripts in an integrated development environment.
Federated learning
Build models
Create a federated learning experiment to train a common model on a set of remote data sources. Share training results without sharing data.
Deployments
Deploy assets
Monitor models
Deploy and run your data science and AI solutions in a test or production environment.
Catalogs
Catalog data
Governance
Find and share your data and other assets.
Metadata import
Prepare data
Catalog data
Governance
Import asset metadata from a connection into a project or a catalog.
Metadata enrichment
Prepare data
Catalog data
Governance
Enrich imported asset metadata with business context, data profiling, and quality assessment.
Data quality rules
Prepare data
Governance
Measure and monitor the quality of your data.
Masking flow
Prepare data
Create and run masking flows to prepare copies of data assets that are masked by advanced data protection rules.
Governance
Governance
Create your business vocabulary to enrich assets and rules to protect data.
Data lineage
Governance
Track data movement and usage for transparency and determining data accuracy.
AI factsheet
Governance
Monitor models
Track AI models from request to production.
DataStage flow
Prepare data
Create a flow with a set of connectors and stages to transform and integrate data. Provide enriched and tailored information for your enterprise.
Data virtualization
Prepare data
Create a virtual table to segment or combine data from one or more tables.
OpenScale
Monitor models
Measure outcomes from your AI models and help ensure the fairness, explainability, and compliance of all your models.
Data replication
Prepare data
Replicate data to target systems with low latency, transactional integrity and optimized data capture.
Master data
Prepare data
Consolidate data from the disparate sources that fuel your business and establish a single, trusted, 360-degree view of your customers.
Services you can use
Services add features and tools to the platform.
watsonx.ai Studio
Develop powerful AI solutions with an integrated collaborative studio and industry-standard APIs and SDKs. Formerly known as Watson Studio.
watsonx.ai Runtime
Quickly build, run and manage generative AI and machine learning applications with built-in performance and scalability. Formerly known as Watson Machine Learning.
IBM Knowledge Catalog
Discover, profile, catalog, and share trusted data in your organization.
DataStage
Create ETL and data pipeline services for real-time, micro-batch, and batch data orchestration.
Data Virtualization
View, access, manipulate, and analyze your data without moving it.
Watson OpenScale
Monitor your AI models for bias, fairness, and trust with added transparency on how your AI models make decisions.
Data Replication
Provide efficient change data capture and near real-time data delivery with transactional integrity.
Match360 with Watson
Improve trust in AI pipelines by identifying duplicate records and providing reliable data about your customers, suppliers, or partners.
Manta Data Lineage
Increase data pipeline transparency so you can determine data accuracy throughout your models and systems.
Where you'll work
Collaborative workspaces contain tools for specific tasks.
Project
Where you work with data.
> Projects > View all projects
Catalog
Where you find and share assets.
> Catalogs > View all catalogs
Space
Where you deploy and run assets that are ready for testing or production.
> Deployments
Categories
Where you manage governance artifacts.
> Governance > Categories
Data virtualization
Where you virtualize data.
> Data > Data virtualization
Master data
Where you consolidate data into a 360 degree view.