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LLM Agents in Production: Architectures, Challenges, and Best Practices
LLMOps
8 mins

LLM Agents in Production: Architectures, Challenges, and Best Practices

An in-depth exploration of LLM agents in production environments, covering key architectures, practical challenges, and best practices. Drawing from real-world case studies in the LLMOps Database, this article examines the current state of AI agent deployment, infrastructure requirements, and critical considerations for organizations looking to implement these systems safely and effectively.
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Building Advanced Search, Retrieval, and Recommendation Systems with LLMs
LLMOps
8 mins

Building Advanced Search, Retrieval, and Recommendation Systems with LLMs

Discover how embeddings power modern search and recommendation systems with LLMs, using case studies from the LLMOps Database. From RAG systems to personalized recommendations, learn key strategies and best practices for building intelligent applications that truly understand user intent and deliver relevant results.
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Building LLM Applications that Know What They're Talking About 🔓🧠
LLMOps
9 mins

Building LLM Applications that Know What They're Talking About 🔓🧠

Explore real-world applications of Retrieval Augmented Generation (RAG) through case studies from leading companies in the ZenML LLMOps Database. Learn how RAG enhances LLM applications with external knowledge sources, examining implementation strategies, challenges, and best practices for building more accurate and informed AI systems.
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LLMOps Lessons Learned: Navigating the Wild West of Production LLMs 🚀
LLMOps
6 mins

LLMOps Lessons Learned: Navigating the Wild West of Production LLMs 🚀

Explore key insights and patterns from 300+ real-world LLM deployments, revealing how companies are successfully implementing AI in production. This comprehensive analysis covers agent architectures, deployment strategies, data infrastructure, and technical challenges, drawing from ZenML's LLMOps Database to highlight practical solutions in areas like RAG, fine-tuning, cost optimization, and evaluation frameworks.
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Demystifying LLMOps: A Practical Database of Real-World Generative AI Implementations
LLMOps
4 mins

Demystifying LLMOps: A Practical Database of Real-World Generative AI Implementations

The LLMOps Database offers a curated collection of 300+ real-world generative AI implementations, providing technical teams with practical insights into successful LLM deployments. This searchable resource includes detailed case studies, architectural decisions, and AI-generated summaries of technical presentations to help bridge the gap between demos and production systems.
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Everything you ever wanted to know about LLMOps Maturity Models
LLMOps
9 mins

Everything you ever wanted to know about LLMOps Maturity Models

As organizations rush to adopt generative AI, several major tech companies have proposed maturity models to guide this journey. While these frameworks offer useful vocabulary for discussing organizational progress, they should be viewed as descriptive rather than prescriptive guides. Rather than rigidly following these models, organizations are better served by focusing on solving real problems while maintaining strong engineering practices, building on proven DevOps and MLOps principles while adapting to the unique challenges of GenAI implementation.
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