Resource library

Watch this keynote from Hanlin Tang on the discussion of the science and engineering behind LLMs in enterprise applications, the choice between buying LLM services from external providers and building custom models, specialized domain-specific models, and more.

Watch this expert panel discuss generative AI in enterprise, focusing on model efficiency, personalization, RAG's role, and aligning AI with business needs.

Watch this AI expert panel discuss the advancements and challenges in LLMs from proof-of-concept to production, the complexities of LLMOps, and regulatory compliance.

Watch this workshop to learn the importance of evaluating LLMs using Fiddler Auditor, strategies to mitigate prompt injection attacks, and developments in LLMs.

In this session, Stanford Professor James Zou discusses how ChatGPT's behavior is changing over time. Discover the fascinating research findings and implications for AI language models.

In this fireside chat with CEOs Krishna Gade and Lukas Biewald, they discuss AI trends, leveraging LLMs, and the complexities of LLM deployment at scale.

Watch this session as Josh Rubin explores the question of whether LLMs can be explained, emphasizing that understanding a model's behavior is crucial for identifying weaknesses, ensuring robustness, and aiding human decision-making.

In this workshop, Murtuza Shergadwala shares his experiences in building the RAG-based Fiddler Chatbot, and provides tips and tricks on prompt engineering, and more.

Watch this panel of industry leaders share their insights on LLM implementation and their significant impact on business ROI in their respective fields.

Watch this demo to explore how the Fiddler AI Observability Platform for MLOps enables data scientists and AI practitioners to monitor, explain, analyze, and improve ML models.

Watch this demo to explore how the Fiddler AI Observability Platform for LLMOps streamlines end-to-end LLMOps by enabling data scientists and AI practitioners.

In this workshop, see how Fiddler users can use the Fiddler Report Generator (FRoG) to create customizable reports for model risk management (MRM) and periodic model health reviews.

Improve LLMs with robustness testing before deploying them to production.

Get a quick overview of the Fiddler AI Observability platform, including monitoring, explainability, fairness, and analytics.

Learn how Fiddler’s unique, clustering-based method accurately monitors data drift in NLP models and LLM-based embeddings.

Learn more about Fiddler Auditor, the open source robustness library that facilitates red teaming of LLMs. Improve LLMs with robustness testing before deploying them to production.

Learn about Fiddler Auditor, the open source robustness library that facilitates red teaming of LLMs. Robustness testing is a critical step in pre-production to minimize hallucinations, bias, and adversarial attacks.

Get a quick overview of the critical role of AI Observability, the foundation you need to standardize LLM and MLOps practices, for building responsible AI.

Learn how to track the performance of LLM-based embeddings from OpenAI, Cohere, Anthropic, and other LLMs by monitoring drift using Fiddler.

Learn how Fiddler AI Observability provides ML teams with a unified platform to deliver and improve high-performing ML models while saving time and money.

Watch this panel session with Ricardo Baeza-Yates, Director of Research at Institute for Experiential AI, Northeastern University; Miriam Vogel, President and CEO at EqualAI, Chair at National AI Advisory Committee; Toni Morgan, Responsible Innovation Manager at TikTok, to learn why responsible AI principles and frameworks are necessary, why model governance is important, and the limitations of LLMs.

Saad Ansari, Director of AI at Jasper AI, dives into how AI can be used for societal benefit, the importance of intentionality when designing new technology, and applications for generative AI.

Hear from Amit Prakash, CTO and Co-founder at ThoughtSpot; Diego Oppenheimer, Partner and CEO in Residence at Factory; and Roie Schwaber-Cohen, Staff Developer Advocate at Pinecone on how LLMOps optimizes MLOps for LLMs, the key pieces of a generative AI workflow, and how ML teams can leverage LLMs in their applications.

Watch this session from Ali Arsanjani, PhD, Head of the AI Center of Excellence at Google, to learn about the importance of explainability, adaptability, and risk-minimizing in enterprise generative AI.

Watch this panel session with Payel Das, Principal Research Staff Member and Manager, Trusted AI a IBM Research; Srinath Sridhar, CEO and Co-founder at Regie.ai; Casey Corvino, CTO and Co-founder at Lavender AI to learn how engineering teams are using generative AI to improve productivity and how IBM is using generative AI to drive medical advances.

Watch this session with George Mathew, Managing Director at Insight Partners to learn about the current state of AI, the different modalities of human and machine interaction, the advancements, and challenges AI presents.

Krishna Gade joins Emerj AI in Financial Service to discuss why financial services compliance is being geared towards verifying the customer from their introduction to the services funnel.

Learn how Fiddler AI Observability provides ML teams with a unified platform to monitor, analyze, explain, and improve ML models at scale, and build trust into AI.

Fiddler’s founder and CEO Krishna Gade sat down with Forward to talk about explainable AI, lessons from Microsoft, Twitter, Pinterest, Facebook, Bing vs. Google, raising $45M and what should entrepreneurs building in AI today focus on.

Krishna Gade, Founder & CEO of Fiddler, discusses how companies should care about explainability and building MLOps systems at scale.

Learn how Fiddler’s custom dashboards help ML teams obtain actionable model insights, and increase organizational alignment and collaboration.

Learn how human-centric approach to designing better frameworks and training to fully utilize the potential of explainable tools.

Learn how to monitor models with unstructured data using Fiddler's cluster-based binning approach.

Learn how to perform root cause analysis to gain contextual insights, and improve model outcomes using Fiddler's analytics capabilities.

Learn how to evaluate and validate models in Fiddler before deploying them into production.

Learn how to get a deep understanding of model predictions using Fiddler's explainability capabilities.

Learn how to quickly detect model performance and drift issues, and reduce the time to troubleshoot issues with root cause analysis using Fiddler.

Learn how Fiddler’s powerful and customizable alerts provide early warning on model performance.

Director of Data Science at Fiddler AI, Joshua Rubin, discusses how he uses machine learning to his advantage and how machine learning is growing.

Watch this on-demand webinar by MLOps Community and Fiddler to learn how MLOps teams can monitor, explain, and analyze their ML models.

Krishnaram Kenthapadi, Chief Scientist at Fiddler, explains the importance of validating models post-deployment and why you should test your AI early and often.

Krishna Gade, Founder & CEO of Fiddler, discusses how companies can address potential bias and other challenges in their AI.

Trustworthy ML is a way of thinking and operationalizing throughout the entire machine learning lifecycle, starting from the problem specification phase.

Operationalize AI/ML in a safe and trustworthy way with Fiddler. Enable your teams to monitor, explain, analyze, and improve their ML models.

Krishnaram Kenthapadi, Chief Scientist at Fiddler AI, explains how model performance management enables real-time model monitoring, alerts, and explainability to help ML teams monitor, explain, analyze, and improve models, while operationalizing ML in a safe and trustworthy way.

Krishnaram Kenthapadi, Chief Scientist at Fiddler AI, explains how to monitor ML models in practice, featuring real-world use cases.

Hima Lakkaraju, assistant professor at Harvard University, summarizes the conclusions and key takeaways for ML model monitoring in practice.

Hima Lakkaraju, assistant professor at Harvard University, explains how to monitor the behavior of ML models through model interpretations and explanations.

Hima Lakkaraju, assistant professor at Harvard University, explains how to monitor the adversarial robustness of deployed ML models.

Krishnaram Kenthapadi, Chief Scientist at Fiddler AI, explains the common model issues that lead to performance degradation and how to monitor ML models.

Learn the human-centric challenges and requirements for ML model monitoring in real-world applications.

Fiddler CEO Krishna Gade sat down with the Montgomery Summit to discuss the need for model performance management and the critical role of explainability for AI solutions.

See the Fiddler Model Performance Management platform in action at AIIA's MLOps Summit.

Krishna Gade, Founder & CEO at Fiddler.ai, talks with John Furrier at Amazon re:MARS 2022.

Learn about FairCanary, a novel approach to generating feature-level bias explanations that is an order of magnitude faster than previous methods.

Krishna Gade, Founder & CEO of Fiddler, sits down with FirstMark to discuss explainability in AI, model drift, bias detection, responsible AI and much more.

Watch a panel discussion on the challenges and limitations of explainability for machine learning in credit underwriting models.

Watch this Rise & IGNITE expert panel discussion on implementing responsible AI.

A panel of industry and research experts discuss the state of explainable AI today and what the future holds.

Scott Zoldi, Chief Analytics Officer at FICO, has authored over 100 patents in ML and AI. He discusses why AI needs to grow up fast and what orgs can do about it.

Anjana Susarla, who holds the Omura-Saxena Professorship in Responsible AI at Michigan State, discusses the different dimensions of responsible AI.

Léa Genuit, a senior machine learning engineer at Fiddler AI, presents at the ML Fairness Summit on intersectional group fairness using worst-case comparisons.

Learn how to stay compliant with AI regulations while maximizing your model performance using explainability and continuous monitoring.

Anand Rao, Global AI Lead, PwC discusses how organizations can implement AI responsibly.

Krishna Gade, CEO & Co-Founder, Fiddler AI, talks with theCUBE's host John Walls for a CUBE Conversation as a part of the AWS Startup Showcase.

Krishna Gade, Founder & CEO of Fiddler, explains how to maximize your model performance using explainability and continuous model monitoring.

Fiona McEvoy, founder of YouTheData.com, shares her perspective on algorithmic bias, deep fakes, emotional AI, and the way that AI systems impact our behavior.

Fiddler Founder & CEO Krishna Gade discusses how explainable AI can address model bias, expedite debugging, and accelerate trust in AI-driven decision making.

Lofred Madzou, AI Lead at the World Economic Forum, shares his experiences managing AI projects around the world and the top things to keep in mind when implementing AI.

Karen Hao, Senior AI Reporter, MIT Technology Review discusses the current state of AI and democratizing its use.

Scott Belsky, Chief Product Officer, Adobe Creative Cloud discusses AI’s role in the creative world and its potential for unleashing the creative mind.

Listen to this episode of the Responsible AI podcast to hear how different companies are defining responsible AI and measuring the impact of AI applications.

Panelists discuss the increasing use cases for AI within finance, the unique regulatory and compliance considerations, and areas of opportunity.

Watch this panel discussion on AI in Finance featuring Wells Fargo, Regions Bank, QuantUniversity, and Google.

Maria Axente, Responsible AI lead for PwC UK, shares what RAI means, why its importance is overlooked, and ways to incentivize teams to implement AI ethically.

Learn how to achieve responsible AI in finance using Model Performance Management.

Krishna Gade, the co-founder and CEO of Fiddler, discusses problems with bias, fairness, and transparency in AI.

Krishna Gade, Founder of Fiddler AI, discusses how to build AI responsibly, shares real-world examples, and shows why explainable AI is critical.

Hear a panel of experts shed light on the state of explainable AI, key considerations for success moving forward, and the latest research and industry trends.

Read about GroupShapley and GroupIG (Integrated Gradients), axiomatically justified methods to understand drift in ML model prediction distributions over time.

Improve your MLOps workflow on SageMaker using Fiddler's Model Performance Management platform.

Learn why fairness metrics need to incorporate intersectionality, including a simple method to expand the definitions of existing group fairness.

Watch this panel discussion on building responsible, ethical, and accountable AI.

Learn how organizations view and use explainability for stakeholder consumption, including a framework for establishing clear goals for explainability.

Learn how subtle differences in the underlying game formulations of existing methods can cause large differences in the attributions for a prediction.

Watch this panel discussion on how to explain opaque model behavior using explainable AI.

Watch a presentation on the practical challenges and lessons learned by experts implementing explainable AI in industry.

Luke Merrick presents to the Data Council on how to put explainable AI into practice.

Experts weigh in on the future of AI, as it rapidly becomes a general-purpose technology, reverberating across several industries.

Learn how randomized feature ablation helps measure the importance of a feature on a model's ability to make good predictions.

An overview of model interpretability and explainability in AI, key regulations/laws, and techniques/tools for providing explainability as part of AI/ML systems

A panel of industry experts discusses the opportunities and challenges in cracking the potential of the multi-hundred billion dollar enterprise AI market.