Operationalizing Machine Learning is challenging:
Production pipeline breaks and damage performance
Black box ML models cause teams to lose visibility and trust
Models degrade without notice and hurt business
With Fiddler, accelerate your ML models into production and confidently maintain high performance.
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Machine learning has unique challenges that are different from a typical software development cycle. The need to continuously validate the business value of AI calls for a new AI Observability approach that provides a 360-degree view of the machine learning lifecycle. Use Fiddler to streamline all machine learning operations in one place and get to the root causes faster with Explainable AI.
MLOps or Machine Learning Operations, the ML equivalent of DevOps, is a collection of ML lifecycle management approaches to streamline, automate, and accelerate ML model deployment to help Data Science (DS) and IT teams.
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Ensure that you have a high-performing, well-tested, and robust model prior to launch with a thorough model validation process. With Fiddler’s Explainable AI, it’s easier to get buy-ins from stakeholders and comply with regulations because any model can be explained in human-understandable terms, increasing trust and transparency.
Track every ML model’s performance with Fiddler, and get notified when performance degradation occurs due to:
Monitor data drift at the feature level and analyze the impact of this drift on the overall model performance
Automatically catch integrity issues such as missing values, type mismatches, and range mismatches
Quickly detect inputs that are outside the bounds of normal queries, including adversarial attacks and maintain high performance.
With XAI, you get visibility into the incident immediately, reducing the time to troubleshoot by 95%! What would take you 3-6 weeks to find, now takes less than 24 hours.
WITHOUT XAI
WITHOUT XAI
Get extensive explanations into the ML issues behind changes in model operation statistics and any corresponding alerts.
