Veritone Benchmark BETA
Build AI Trust and Explainability
Gain visibility into the behavior and performance of AI models, building trust and explainability and reducing risk.
WHO BENEFITS:
Enterprise Business Leaders
Insure your strategic AI projects against unforeseen results such as bias by clearly understanding performance metrics, behavioral traits, and risk levels of your AI models. Maximize project success and minimize unpredictable AI behavior that could have devastating consequences.
AI Development Teams
Gauge performance and behavior of AI models and easily compare results across AI models to deploy the most optimal models for the job - whether the models are homegrown, third party, or aiWARE based. Evaluate models initially and monitor their performance over time.
System Integrators
Deliver projects leveraging transparent and trusted AI models from the aiWARE platform or other models. Continue monitoring your AI models as data inputs change over time to ensure optimal performance and risk levels. Easily evaluate new AI models as AI technology evolves.
Technology Providers
Integrate benchmarking into your own AI-based applications to reassure your users that the digital workers they depend on are producing the most reliable and trustworthy results.
Ensure AI Model Success
Many AI projects fall short of expectations due to poor model performance or the unintended consequences of inaccurate AI decisions. From a text extraction model producing inaccurate output requiring correction, to a facial recognition model failing to correctly identify race, to a job seeker evaluator model preferring male candidates, AI model failures can stop an AI project dead in its tracks.
Organizations implementing AI need a universal way to evaluate and monitor the performance and behavior of their AI models, both pre-deployment and ongoing, no matter the vendor or features used.
Veritone Benchmark helps organizations easily evaluate, compare, and monitor performance and behavior across AI models, whether homegrown, third party, or aiWARE-based, building AI model trust and explainability. Select the best AI model for the job, detect drift and correct it to achieve better business outcomes.
Evaluate and Explain Your AI Models
Veritone Benchmark provides easy to understand dashboard views on key metrics for AI model effectiveness, to enable engine evaluation, comparison, and ongoing monitoring in production. Dashboard metrics include accuracy, speed, cost, word error rate, and overlap. AI model support includes transcription, translation, facial detection and recognition, object detection, and logo detection.
Benchmark explains the behavior of AI models by measuring and reporting on the factors or features that influence its decision making. For example, an audio transcription AI model’s features that explain its behavior and output might include audio quality, background noise, multiple speakers, subject matter, speaking speed, accent, gender, and age.
Use Benchmark on your own AI models, another vendor’s models, or aiWARE’s ecosystem of hundreds of audio, video, text and data extraction models.
Capabilities
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Future Proof AIPre-integrated with the aiWARE Enterprise AI platform, Benchmark delivers model evaluation across an ecosystem of the best AI
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AI Model CertificationFoster trust in your AI models by proudly displaying Veritone’s Benchmark certification seal. Veritone’s innovative AI model certification process ensures that your model meets minimum acceptable performance and explainability standards for that cognitive category.
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Automatic Drift Detection and NotificationPre-integrated with the Veritone Automate Studio low-code workflow designer, Benchmark’s ongoing monitoring detects and automatically responds to model drift by comparing questionable results to the baseline, and if a drift trend is detected, triggering a notification for human review and model re-evaluation with Benchmark.
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Universal Across ModelsPerform universal accuracy comparisons across models – whether custom, third party, or aiWARE – with a single tool and dashboard view, making performance evaluation across models a snap.
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Initial and Ongoing MonitoringCreate model scorecards to gauge initial model performance and identify risk early. Monitor on an ongoing basis to detect and correct model drift or re-evaluate as models change or need replacing.
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Pre-Built and Custom Data SetsGet started quickly with pre-built AI model dashboards for select aiWARE engines and data sets, with the additional flexibility of importing custom data sets for homegrown or third party model evaluation.
