With the emergence of mainstream LLMs like GPT-3, Google Gemini, and DeepSeek, big models are becoming a commodity. As LLMs train on shared public datasets, companies must prioritize AI-ready data over mere OpenAI API integration to stay competitive.

This session explores how industries can achieve AI-ready data to navigate the “first-party data-pocalypse.” We’ll discuss the risks of inaction and the importance of investing in data collection and infrastructure.

What we will cover:
Understand the current state of reliable AI in enterprise industries.
Explore the evolving AI stack, the role of first-party data, and the foundations of data and AI observability.
Learn the data architectures and processes for building reliable generative AI, including RAG and fine-tuning.
Learn best practices for detecting, triaging, and resolving data incidents to ensure GenAI reliability with AI-ready data.

#generativeai #rag #datascience #firstpartydata #dataarchitecture
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Table of Content:

0:00 Introduction and Importance of Data in AI Applications
5:13 Key Factors for AI-Ready Data
8:52 Why AI Applications Break
18:25 Monte Carlo’s Approach to AI Reliability
20:46 Monte Carlo's use of AI in observability
22:37 Demo: Monte Carlo Platform in Action
33:19 Operational aspects of Monte Carlo
37:00 How costs scale with data growth
39:04 Explanation of Monte Carlo's pricing model

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