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LLMOps Is About People Too: The Human Element in AI Engineering
LLMOps
9 mins

LLMOps Is About People Too: The Human Element in AI Engineering

We explore how successful LLMOps implementation depends on human factors beyond just technical solutions. It addresses common challenges like misaligned executive expectations, siloed teams, and subject-matter expert resistance that often derail AI initiatives. The piece offers practical strategies for creating effective team structures (hub-and-spoke, horizontal teams, cross-functional squads), improving communication, and integrating domain experts early. With actionable insights from companies like TomTom, Uber, and Zalando, readers will learn how to balance technical excellence with organizational change management to unlock the full potential of generative AI deployments.
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Streamlining LLM Fine-Tuning in Production: ZenML + OpenPipe Integration
LLMOps
15 mins

Streamlining LLM Fine-Tuning in Production: ZenML + OpenPipe Integration

The OpenPipe integration in ZenML bridges the complexity of large language model fine-tuning, enabling enterprises to create tailored AI solutions with unprecedented ease and reproducibility.
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Building a Pipeline for Automating Case Study Classification
LLMOps
6 mins

Building a Pipeline for Automating Case Study Classification

Can automated classification effectively distinguish real-world, production-grade LLM implementations from theoretical discussions? Follow my journey building a reliable LLMOps classification pipeline—moving from manual reviews, through prompt-engineered approaches, to fine-tuning ModernBERT. Discover practical insights, unexpected findings, and why a smaller fine-tuned model proved superior for fast, accurate, and scalable classification.
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Query Rewriting in RAG Isn’t Enough: How ZenML’s Evaluation Pipelines Unlock Reliable AI
LLMOps
8 mins

Query Rewriting in RAG Isn’t Enough: How ZenML’s Evaluation Pipelines Unlock Reliable AI

Are your query rewriting strategies silently hurting your Retrieval-Augmented Generation (RAG) system? Small but unnoticed query errors can quickly degrade user experience, accuracy, and trust. Learn how ZenML's automated evaluation pipelines can systematically detect, measure, and resolve these hidden issues—ensuring that your RAG implementations consistently provide relevant, trustworthy responses.
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Production LLM Security: Real-world Strategies from Industry Leaders 🔐
LLMOps
8 mins

Production LLM Security: Real-world Strategies from Industry Leaders 🔐

Learn how leading companies like Dropbox, NVIDIA, and Slack tackle LLM security in production. This comprehensive guide covers practical strategies for preventing prompt injection, securing RAG systems, and implementing multi-layered defenses, based on real-world case studies from the LLMOps database. Discover battle-tested approaches to input validation, data privacy, and monitoring for building secure AI applications.
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Optimizing LLM Performance and Cost: Squeezing Every Drop of Value
LLMOps
7 mins

Optimizing LLM Performance and Cost: Squeezing Every Drop of Value

This comprehensive guide explores strategies for optimizing Large Language Model (LLM) deployments in production environments, focusing on maximizing performance while minimizing costs. Drawing from real-world examples and the LLMOps database, it examines three key areas: model selection and optimization techniques like knowledge distillation and quantization, inference optimization through caching and hardware acceleration, and cost optimization strategies including prompt engineering and self-hosting decisions. The article provides practical insights for technical professionals looking to balance the power of LLMs with operational efficiency.
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The Evaluation Playbook: Making LLMs Production-Ready
LLMOps
7 mins

The Evaluation Playbook: Making LLMs Production-Ready

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.
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Prompt Engineering & Management in Production: Practical Lessons from the LLMOps Database
LLMOps
7 mins

Prompt Engineering & Management in Production: Practical Lessons from the LLMOps Database

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.
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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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