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Virtual | Nov 9 | 10:00 AM – 3:00 PM PT

LLMs in the Enterprise:
From Theory to Practice

Watch the recordings of AI Forward 2023, a one-day, dual-track virtual summit, bringing together industry leaders and practitioners from data science, machine learning, risk and compliance, and AI policy.
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Generative AI is being rapidly deployed across industries to unlock business benefits, but there are everyday challenges in working with, operationalizing, and productizing LLMs that need to be addressed first. From evaluating LLM robustness and continuous monitoring to using safety metrics, organizations are deploying cutting edge ways to get the most out of their models.

We will discuss how to maximize the value of AI investments, and you can see the Fiddler AI Observability solutions for predictive and generative AI in action.

Watch to learn:

LLMOps challenges and opportunities in pre- and post-production
Addressing the infrastructure challenges of LLMs
Enterprise use cases that are delivering valuable business impact
The core capabilities of the Fiddler AI Observability platform and Fiddler Auditor

All Recordings

Journey of LLMs in the Enterprise
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Krishna Gade highlights the transformative potential and rapid growth of generative AI applications, and their capability to revolutionize industries.
The Science and Engineering Behind LLMs
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In this keynote presentation, Hanlin Tang discusses the science and engineering behind LLMs in enterprise applications.
Everyday Challenges and Opportunities with LLMs Pre- and Post-Production
AI Forward Summit - LLMs in the Enterprise video image cover for Everyday Challenges and Opportunities with LLMs Pre- and Post-Production
In this panel, AI experts delved into the dynamic realm of generative AI within enterprise settings, highlighting key aspects such as the critical need for enhancing model efficiency, personalization strategies, and addressing the challenges confronting developers entering the AI domain.
LLM POC to Production: Insights From an Infrastructure Viewpoint
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In this panel, the AI experts discuss on the advancements and challenges in LLMs from proof-of-concept to production, focusing on infrastructure, model management, and regulatory compliance.
How is ChatGPT’s Behavior Changing Over Time?
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In this presentation, Stanford Associate Professor James Zou discusses the dynamic changes in ChatGPT's behavior over time, highlighting substantial alterations in its ability to follow instructions and improvements in safety measures.
Fireside Chat: CEOs in Conversation
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In this fireside chat, Lukas and Krishna discussed the rising potential of search, summarization, and chatbot applications in leveraging LLMs, emphasizing their transformative impact on business operations.
Can LLMs be Explained?
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In this session, Joshua Rubin shares the importance of explainability in AI, emphasizing that understanding a model's behavior is crucial for identifying weaknesses, ensuring robustness, and aiding human decision-making.
Leadership Perspectives: Use Cases and ROI of LLMs
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In this panel, industry leaders shared their insights on LLM implementation and their significant impact on business ROI in their respective fields.
Immersive Gen AI Experience
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In this workshop, Joshua Rubin deep dives into an immersive GenAI experience to see how generative AI models, particularly those handling unstructured data like natural language or multimodal inputs, differ from traditional machine learning models.
Evaluating LLMs with Fiddler Auditor
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In this workshop, Amal Iyer discussed the importance of evaluating LLMs using Fiddler Auditor, the open-source robustness library for red-teaming of LLMs, designed for testing and ensuring the reliability of LLMs in various applications.
Effective Enterprise Compliance: Make a Leap of Trust with FRoG (Fiddler Report Generator)
AI Forward Summit - LLMs in the Enterprise video image cover for Effective Enterprise Compliance: Make a Leap of Trust with FRoG (Fiddler Report Generator)
In this workshop, Bashir Rastegarpanah shows how Fiddler users can use the Fiddler Report Generator (FRoG) to create customizable reports for model risk management (MRM) and periodic model health reviews.
Fiddler AI Observability Platform for ML and LLMOps
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In this workshop, Sabina Cartacio and Barun Halder demonstrate how the Fiddler AI Observability platform offers comprehensive support for MLOps and LLMOps, enabling data scientists, AI practitioners, business stakeholders, auditors, and regulators to validate, monitor, analyze, and improve predictive and generative models.
Chat on Chatbots: Tips and Tricks
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In this workshop, Murtuza Shergadwala shares his experiences in building the RAG-based Fiddler Chatbot, and provides tips and tricks on prompt engineering, document chunking, managing hallucinations in responses, improving user trust through UI/UX design, and improving the chatbot with evolving documentation and user feedback.

Speakers

Hanlin Tang
Databricks
CTO of Neural Networks, Co-founder of MosaicML
Sara Hooker
Cohere
Director of Cohere for AI
Juan Bustos
Google
Lead Solutions Consultant, AI Center of Excellence
Radhika Venkatraman
Startups and CDOs
Strategic Advisor
Jerry Liu
LlamaIndex
CEO and Co-founder
Lukas Biewald
Weights & Biases
CEO and Co-founder
Alan Ho
DataStax
VP Product, AI 
Nirmalya De
NVIDIA
Principal Product Manager, Conversational AI and Deep Learning 
Peter Bailey
Canva
ML Engineering Lead, Search and Recommendations
Vida Williams
Virginia Alcoholic Beverage Control Authority
​​Chief Digital and Branding Officer
James Zou
Stanford University
Assistant Professor of Biomedical Data Science and of Computer Science & Electrical Engineering
Jeff Huber
Chroma
CEO and Co-founder
Krishna Gade
Fiddler AI
CEO and Co-founder
Lior Berry
Fiddler AI
Director of Engineering
Kirti Dewan
Fiddler AI
CMO
Sree Kamireddy
Fiddler AI
VP of Product
Sabina Cartacio
Fiddler AI
Staff Product Manager
Joshua Rubin
Fiddler AI
Director of Data Science
Amal Iyer
Fiddler AI
Staff Data Scientist
Murtuza Shergadwala
Fiddler AI
Senior Data Scientist
Bashir Rastegarpanah
Fiddler AI
Data Scientist
Barun Halder
Fiddler AI
Staff Software Engineer