E X P L A I N
Understand the how and the why
behind your ML models' decisions
Gain actionable insights that ensure model accuracy, fairness, and transparency
for better informed business decisions and necessary regulatory compliance.
Do you struggle to understand why a model you've built is behaving a certain way?
Explainability algorithms can be used to identify models that might perform well on paper, but don’t have good generalization in practice. Explainable AI (XAI) is crucial for organizations who need to model governance to keep ML within risk requirements to avoid non-compliance fines.
The most important AI capability
Upcoming legislation like the EU AI Act, which focuses on ethics, could prompt companies to implement XAI more comprehensively. In a 2021 report by CognitiveScale, 34% of C-level decision-makers said that the most important AI capability is “explainable and trusted.”
Check out our guide on getting started with explainability in machine learning!
Build trust in your ML pipelines
with Seldon’s advanced explainable AI capabilities
Gain stakeholder trust in ML models
- via multi-model serving with overcommit functionality
Interpret model behavior and individual predictions
- with extended inference graphs
Maintain compliance of
Global AI regulations
- optimized for popular ML frameworks and custom language wrappers
Implement explainability for all use cases
- by deploying ML models using enterprise APIs with SDK
De-bug and re-train your ML model
- with traffic splitting deployment strategies like canary and A/B testing
Identify and mitigate common biases in judgements
- with model workflow and configuration wizards that decrease time-to-value
Covéa achieves an 11x ROI in just 6 months
“Seldon Deploy gave us the flexibility we needed to be able to manage the disparate policy data we had. Once models are deployed we are now able to integrate our many data sets with explainability.”
– Tom Clay, Chief Data Scientist at Covéa