Build Responsible AI with Mission-Critical AI Observability

Fiddler is a pioneer in AI Observability — the foundation you need to standardize LLM and MLOps practices. AI Observability is reliant not only on metrics, but also on how well issues can be explained when something eventually goes wrong.

Fiddler partners with government agencies with a proven AI Observability platform for sophisticated model development and deployment along with advanced AI/ML techniques. 

Government Agencies Trust Fiddler with AI Innovation

• ‍Government trusted: Trusted by government agencies that aim to accelerate AI innovation to strengthen national security.‍
• Secure deployment: Available on AWS GovCloud, one of the largest secure cloud solutions that addresses the most stringent U.S. government security and compliance requirements.‍
• Petabyte-scale: Enables government agencies with petabyte scalability to support large AI deployments.‍
• Air gapped deployment: Air gapped deployments to accelerate mission-critical projects that require little or no connectivity.‍
• Edge support: AI Observability for edge devices, giving agencies widespread flexibility for high performance monitoring and explainable AI.‍
• Trusted advisors and partners: White glove support from Fiddler’s AI science experts, dedicated to ensuring the success of your mission-critical ML and LLM projects. 
  

President Biden's Executive Order on safe, secure, and trustworthy AI is set to be implemented across the United States. It requires the adoption of AI monitoring systems, like Fiddler, by August 1, 2024, for applications that affect rights (e.g., underwriting) or safety (e.g., autonomous vehicles).

Key Capabilities

Monitoring 

Monitor predictive models, NLP, CV, and LLMs in pre and post-production and manage all performance metrics at scale in a unified dashboard. From alerts to root cause analysis, pinpoint areas of model underperformance and minimize impact. You can also find quick answers to the root cause and the “why” behind all issues. 

Plug Fiddler into your existing ML and LLM tech stacks for consolidated monitoring to: 

• Receive real-time alerts on potential threats from image data inconsistencies
• Improve target precision with high performance model monitoring across different battlefield environments 
• Monitor changes in traffic, signals, and communication patterns

Explainable AI (XAI)

Fiddler uses proprietary explainable AI technology to provide complete context and visibility into ML model behaviors and predictions, from training to production. Implement powerful XAI techniques at scale to build trusted AI solutions that help you: 

• Identify anomalies to anticipate potential threats and navigation
• Image model detection by using the inferences of the surrounding area
• Track differences in operational domain of imagery or sensor data compared to training-time
• Enhance human decision-making in high-stakes post-mission analysis scenarios

Analytics 

Analytics must deliver actionable insights that power data-driven decisions. To improve predictions, market context and business alignment must be baked into modeling so results reflect the needs and challenges of your business. 

Use descriptive and prescriptive analytics from ML models and LLMs to make decisions so you can: 

• Incorporate human-in-the-loop process for assessment and decision making
• Align decisions to stay in lockstep with operation needs 
• Respond quickly and refine models when mission or environmental dynamics shift 

Fairness 

Responsible AI is the practice of building transparent, accountable, ethical, and reliable AI. The first step is detection and mitigation of bias in tabular and unstructured datasets and ML models, but you must also support internal governance processes and reduce risk through human involvement. 

Build and deploy responsible AI solutions with bias detection and fairness assessment in order to: 

• Reduce risk by instilling trust with continuous AI monitoring and human decision-making with ML 
• Provide visibility and governance to internal oversight teams
• Mitigate model bias through the detection, comparison, and measurement of dataset bias

Use cases

• Image Explainability for Autonomous Vehicles
• Model Monitoring Sensor Data
• NLP and CV Monitoring for Intelligence and Surveillance