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Making Enterprise GenAI Safe and Effective - Tools and Approaches
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Running prompts at CI does not make your GenAI app enterprise ready
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The BEST component for your RAG system
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Why AI apps don't work in prod: AI Reliability Survey
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48:45
What It Actually Takes to Deploy GenAI Applications to Enterprises Custom Evaluation Models
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Lessons learned from scaling large language models in production
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38:26
From Idea to Production: AI Infra for Scaling LLM Apps
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46:11
LLM Fine-Tuning for Modern AI Teams: How One E-Commerce Unicorn Cut Inference Cost by 90%
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Function Calling for LLMs: RAG without a Vector Database
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Finding training inefficiencies with CentML DeepView
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Evaluating LLMs and RAG Pipelines at Scale
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Empowering Data Science Teams: Harnessing AI with Appen
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Better Chatbots with Advanced RAG Techniques
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Enhance Cost Efficiency in Domain Adaptation with PruneMe
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Data Versioning in Generative AI: A Pathway to Cost-effective ML
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Building ML and GenAI Systems with Metaflow
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Efficiently Fine-Tune And Serve Your Own LLMs
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The Who, What, and Why of Data Lake Table Formats
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Private, Local AI
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The Journey of Building a Leading Open Source LLM Security Toolkit
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The Secret Sauce for Deploying LLM Applications into Production
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Running Multiple Models on the Same GPU, on Spot Instances
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Towards Robust GenAI: Techniques for Evaluating Enterprise LLM Applications
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Introducing Arize-Phoenix and OpenInference
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Mitigating RAG Hallucinations with Aporia Guardrails
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Evaluation Engineering: Iterative Strategies to Testing Prompts
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Customizable RAG Workflows with your Own Data
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Wanted: A Silver Bullet MLOps Solution for Enterprise
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1:40:21
Evaluation Techniques for Large Language Models
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