IBM Watson OpenScale

Explore the analyst report

IBM is a leader in The Forrester Wave™: Multimodal Predictive Analytics and Machine Learning, Q3 2020

Operate trusted AI. Monitor and manage models.

Businesses are increasingly reliant on AI to speed decision making and streamline operations. Successful AI adoption relies on trust and transparency in how models are built, deployed and managed.

IBM Cloud Pak® for Data helps you gain trust and transparency in AI. IBM Watson® OpenScale™, a capability within IBM Watson Studio on IBM Cloud Pak for Data, monitors and manages models to operate trusted AI. With model monitoring and management on a data and AI platform, an organization can:

Monitor model fairness, explainability and drift
Visualize and track AI models in production
Validate and test models to mitigate regulatory, reputational and operational risks
Increase end-to-end visibility of AI lifecycles

→ Read the complementary ModelOps research

→ Watch the data science on-demand webinars (link resides outside IBM)

Turn open source frameworks and tools into trusted AI models with IBM Cloud Pak for Data.

IBM Watson OpenScale: Scale, trust and accelerate value from AI

Benefits

Promote trust and transparency

Quantify the potential benefits of model monitoring. Make your models more accurate and transparent.

Read the analyst report

Manage model fairness

Keep models fair and explainable. Implement passive or active de-biasing as required.

Understand de-biasing

Get more from your AI investment

Continuously manage model performance. Improve AI outcomes by minimizing model drift.

Dig deeper into drift

Manage model compliance and risk

Track AI model risk across workflows. Share model insights with stakeholders and keep those insights as an AI system of record.

View the infographic (PDF, 133 KB )

Optimize AI and cloud deployment

Manage models across any cloud with unified data and AI services. Speed time to AI value by automating the AI lifecycle.

Understand the economic value

Extend your open source strategy to trusted AI

Make AI more trustworthy and transparent with your open source investment.

Read the blog

Features

Explain AI models

View and understand models. Interpret charts on model insights.

Watch the video (01:33)

Detect AI model bias

Measure fairness and mitigate potential bias.

Watch the video (02:46)

Visualize model drift

Continuously monitor models. Alert users on drift in model accuracy and data consistency.

Watch the video (01:58)

Track model quality

Monitor deployment responses and feedback data.

View the quality dashboard

Implement model governance

Validate and deploy models with end-to-end visibility.

See how to govern models

Manage model risks

Track outcomes from AI models across its lifecycle and validate models.

Compare models and understand risks

Use cases

Credit risk modeling

Credit lenders can monitor risk models for performance, bias and explainability to limit risk exposure from regulations and create more fair and explainable outcomes for customers.

Explainable claims processing

Insurance underwriters can use machine learning to more consistently and accurately assess claims risk, ensure fair outcomes for customers and explain AI recommendations for regulatory and business intelligence purposes.