The Evolution of Data Infrastructure in the Age of LLM

Overview

Explore how Large Language Models are reshaping modern data infrastructure, examining key architectural changes and emerging best practices for scalable AI systems.

Syllabus

• Introduction to Large Language Models (LLMs)
Overview of LLMs and their capabilities
Historical development of LLMs
Key examples and applications
• Traditional vs. Modern Data Infrastructure
Overview of traditional data infrastructure
Limitations of traditional systems in handling LLMs
Introduction to modern data infrastructure concepts
• Architectural Changes in Data Infrastructure
Distributed computing and storage solutions
Cloud-based infrastructure
Edge computing and its relevance
• Scalability in AI Systems
Challenges of scaling AI models
Techniques for scaling LLMs
Case studies of scalable LLM deployments
• Data Management for LLMs
Data collection and preprocessing strategies
Data pipeline optimization
Handling large datasets and real-time processing
• Integration of LLMs into Existing Systems
API-driven architectures
Microservices and modular design approaches
Strategies for maintaining legacy systems
• Emerging Best Practices
Security and privacy in LLM deployment
Model performance monitoring and optimization
Sustainable AI practices
• Case Studies
Industry-specific implementations of LLMs
Lessons learned from real-world deployments
Future trends and opportunities
• Conclusion
Recap of key concepts
The future of data infrastructure in a world with advanced LLMs
Open discussion and next steps for learners

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Subjects

Data Science