ZenML Blog

A comprehensive exploration of real-world lessons in LLM evaluation and quality assurance, examining how industry leaders tackle the challenges of assessing language models in production. Through diverse case studies, the post covers the transition from traditional ML evaluation, establishing clear metrics, combining automated and human evaluation strategies, and implementing continuous improvement cycles to ensure reliable LLM applications at scale.

Practical lessons on prompt engineering in production settings, drawn from real LLMOps case studies. It covers key aspects like designing structured prompts (demonstrated by Canva's incident review system), implementing iterative refinement processes (shown by Fiddler's documentation chatbot), optimizing prompts for scale and efficiency (exemplified by Assembled's test generation system), and building robust management infrastructure (as seen in Weights & Biases' versioning setup). Throughout these examples, the focus remains on systematic improvement through testing, human feedback, and error analysis, while balancing performance with operational costs and complexity.

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.

ZenML 0.71.0 features the Modal Step Operator for fast, configurable cloud execution, dynamic artifact naming, and enhanced visualizations. It improves API token management, dashboard usability, and infrastructure stability while fixing key bugs. Expanded documentation supports advanced workflows and big data management.

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.

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.

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.

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.

Discover how organizations can successfully bridge the gap between academic machine learning research and production-ready AI systems. This comprehensive guide explores the cultural and technical challenges of transitioning from research-focused ML to robust production environments, offering practical strategies for implementing effective MLOps practices from day one. Learn how to avoid common pitfalls, manage technical debt, and build a sustainable ML engineering culture that combines academic innovation with production reliability.