Explainable Monitoring: Complete visibility with fast problem solving

Continuously monitor the key operational challenges in AI - data drift, outliers and model decay. Get a fast turnaround to real-time issues with explainability and model analytics to ensure business value is intact.

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Turbocharge ML Operations with Sagemaker & Fiddler

Fiddler + AWS Sagemaker customers are empowered to not only train and deploy models (with Sagemaker), but seamlessly monitor, explain, and analyze models to build trustworthy, transparent, and reliable AI systems.

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Whitepaper: The Rise of MLOps Monitoring

How do you successfully deploy robust AI solutions with the last mile in MLOPs - ML Monitoring? What are the key considerations and approaches to use? Learn more in this whitepaper which includes:

• A rundown on the evolution of Monitoring
•The 7 key challenges for ML Monitoring
• An overview of the capabilities and tools needed to solve these challenges

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Build responsible AI systems with trust, visibility, and insights built-in

Fiddler’s Explainable AI Platform enables companies to explain, monitor and analyze their AI solutions to drive successful AI deployments, build trustworthy and responsible AI systems, and bring transparency and positive business impact.

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Monitor, explain, and
analyze your AI in production.

Fiddler's Explainable AI Platform unlocks the AI blackbox with continuous Monitoring and 360-degree Explainability. Get complete visibility into AI systems, understand the "why" behind AI predictions, and drive business impact with actionable insights from Explainable ML Monitoring.

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Fiddler Labs recognized as a Cool Vendor by Gartner in the Cool Vendors in Enterprise AI Governance and Ethical Response.

Explainable Monitoring for Complete Visibility + Fast Problem Solving

Enable your teams to shine light into the AI black box, increase transparency and reliability, and gain actionable insights. Unlock the full value of your AI by building in trust.

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Continuously monitor, explain, and analyze AI systems at scale. With actionable insights build trustworthy, fair, and responsible AI monitoring.

Explainable Monitoring

AI in production is different and more complex than in training. Performance fluctuations can be staggering. Continuous model monitoring and Explainable AI help:

Find and solve data drift issues quickly to ensure end-users are well served

Understand the ‘why’ behind problems using explanations to efficiently root cause issues

Detect and address outliers and to ensure continued high-performance

Explain Blackbox AI

The problem: complex AI systems are inherently black boxes with minimal insight into their operation.

The solution: Explainable AI or XAI makes these AI black boxes more like AI glass-boxes by enabling users to always understand the ‘why’ behind their decisions.

The benefit: Identify, address, and share performance gaps and biases quickly for AI validation and debugging.

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Solve Operational Challenges in AI

Given the complex operational challenges in ML, like data drift and outliers, maintaining high-performance is difficult. Continuous model monitoring and Explainable AI help:

Efficiently solve operational challenges like drift, and outliers with always-on real-time explainable ML monitoring.

Get deep model-level actionable insights to understand problem drivers using explanations and efficiently root cause issues.

Give data scientists immediate visibility into performance issues and resolve them before it results in negative business impact.

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Build Responsible AI

Responsible AI is the practice of building AI that is transparent, accountable, ethical, and reliable.

Culture of accountability: AI impacts lives, making it imperative that the system is governable and auditable through human oversight.

Ethics at the forefront: building responsibly means AI outcomes and predictions are ethical, fair, and inclusive.

Consistent monitoring: continuous real time monitoring of AI ensures precise and rapid error detection with insight into the ‘why’.

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Seamless Pluggability

Fiddler's out-of-the-box integrations are easily pluggable with existing data and Al infrastructure making it flexible to use

Plug into any model framework

Ingest from any data source

Cutting-Edge Explainable AI

Trust: Explaining a black box with another black box does not establish trust. We ensure transparency through visibility.

Production-quality: Fiddler augments top AI explainability techniques in the public domain including Shapley Values and Integrated Gradients to enhance performance.

Enterprise scale: Our solutions are built at enterprise scale and power our industry leading explainability toolset for a robust and reliable experience.

Research-focused: Fiddler's paper 'Explanation Game' introduces confidence intervals and contrastive explanations - a significant upgrade to other Shapley Values implementations.

What our customers are saying

"At Hired, our mission is to match people with a job they love, and doing that at scale requires advanced technology like AI. Fiddler helps enhance our understanding of the AI algorithms at the heart of this candidate matching process by comparing these insights and explanations with our internally developed solutions to empower our data science and curation teams"

Mehul Patel

CEO, Hired

“We use Fiddler’s Explainable AI Platform to not only operationalize our AI models, but more importantly to explain these models. Explanations make these models more understandable across our technical and business teams. Fiddler’s Platform provides in-depth insights that our technical team uses to improve our model input selection and feature engineering process. It also provides human-readable and prescriptive insights to our sales and marketing teams enabling them to more intuitively score leads and prospects to ensure customer and business success. I’m excited about Fiddler’s technology helping us advance our analytics efforts at Rubrik.”

Shibin Nambiar

Staff Data Engineer, Rubrik

Build trustworthy and explainable AI solutions with Fiddler.

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