IBM AI Governance is a one stop solution for transparent, explainable AI model management.
How it’s used
Data access and management
Right data, at the right time for the right user
Use fairness scores to improve bias reduction. Provide complete views of quality, secured data for permission-based, self-service analysis.
AI lifecycle management
Automate the AI model lifecycle
Build and deliver models using automated views of fairness and quality. MLOps decreases errors and improves workload efficiency.
Build trust in processes
Build and governed, explainable AI
Mitigate bias, risk and drift for transparency and explainability of results across each lifecycle stage: develop, deploy and validate.
Dive deeper with the ebook
Achieve trust in data, models and processes with a data fabric, and view real industry examples.
Capabilities of MLOps and trustworthy AI
Activate business-ready data for AI and analytics with intelligent cataloging, backed by active metadata and policy management.
Automate with MLOps
Automate manual tasks that data science must complete as they build and train predictive machine learning models across the entire AI lifecycle.
Embedded decision optimization
Enable data science teams to capitalize on the power of prescriptive analytics and build solutions using machine learning and optimization.
Deploy AI projects across on-premises, on public and private clouds with the data fabric. Promote trust and confidence with trustworthy AI.
MLOps lifecycle governance
Manage regulatory, compliance, risk and more. Keep AI models explainable and transparent. Minimize overhead of manual inspection and costly errors.
MLOps and Trustworthy AI case studies
Learn how IBM customers are using a data fabric built with IBM Cloud Pak for Data to create innovative solutions.
Use IBM Cloud Pak for Data to build these MLOps and trustworthy AI solutions into your data fabric.