Reimagine data management
Address your pressing data challenges with a holistic solution
Build, scale and govern AI models with a data fabric
AI insights require the right data, automated modeling tools and governance to ensure transparency and trust.
Automated data orchestration, model building, deploying and governance can negate risks and help drive accuracy, accountability and responsible AI.
How it’s used
Data access and management
Right data, at the right time for the right user
Provide a complete view of relevant, quality and secured data, ready for permission based, self-service analysis.
AI lifecycle management
Automate the AI model lifecycle
Implement MLOps, decrease human error and increase workflow efficiency for continuous model building and delivery.
Build trust in processes
Build and governed, explainable AI
Mitigate bias, risk and drift, ensuring transparency and explainability of results at each stage of the AI lifecycle.
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-prem, on public and private clouds. 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.
Get the trial at no cost
Automate AI governance with a data fabric.
Working with IBM, we’ve transformed advanced analytics using open and transparent methodologies.
Chief Data and Analytics Officer