Slow Data, Fast Data and Adapting AI models for your business
Sep 1, 2024
auto_awesome
Explore the rapidly evolving landscape of AI models and discover effective strategies for implementation. Delve into the concepts of 'fast data' and 'slow data' and understand how they impact business applications. Learn about Retrieval Augmented Generation and the critical decisions of building, buying, or using open-source models. The discussion emphasizes the importance of adapting AI models to meet evolving business needs and the significance of community feedback in this dynamic environment.
21:12
AI Summary
Highlights
AI Chapters
Episode notes
auto_awesome
Podcast summary created with Snipd AI
Quick takeaways
Understanding the distinction between fast data and slow data is essential for effectively managing AI model updates and relevance.
Businesses must develop a structured framework for AI model integration that considers specific use cases and data characteristics to maintain competitiveness.
Deep dives
The Importance of AI and Cloud Innovations
AI and cloud innovations are significantly changing the landscape of technology, necessitating cloud professionals to stay informed about these developments. The episode emphasizes the rapid pace of change in AI technologies and outlines the importance of adapting business processes accordingly. To effectively manage this transformation, businesses must consider how to integrate their data into AI models, focusing on the frequency of updates and alignment with applications. The discussion highlights that the integration of new data is crucial for improving AI applications and maintaining a competitive edge in a fast-evolving industry.
Understanding Fast Data vs. Slow Data
The distinction between fast data and slow data is pivotal when developing and tuning AI models for various business applications. Slow data refers to information that changes infrequently, such as regulatory updates or seasonal inventory, allowing companies to embed it into AI models with less frequent adjustments. Conversely, fast data represent rapidly changing information, requiring continuous updates to maintain model relevance and accuracy. Utilizing approaches like Retrieval Augmented Generation (RAG) facilitates effective management of fast data by integrating real-time information into AI models, thereby improving decision-making processes.
Framework for Developing AI Models
A proposed taxonomy for AI model development categorizes approaches based on the specific use case and data characteristics, providing a structured way to analyze options. The framework includes large static models for traditional predictive AI, generative AI using large language models, slow-moving data integration, and fast-moving data management via RAG techniques. Each category presents unique considerations regarding economic implications, technological capabilities, and team expertise required for successful implementation. By utilizing this framework, organizations can better align their AI model strategies with their business objectives and resource constraints.
With so much rapid change happening around AI models, is there a framework for when to build models, train models, fine-tune models, use RAG or something else? Let’s explore…
Secure your seat for Gartner IT Infrastructure, Operations & Cloud Strategies Conference 2024 with our early bird savings of $400 off standard rates now.