# MLOps World: Machine Learning in Prouction

> A Uniquely Interactive Experience2nd Annual MLOps World Conference on Machine Learning in Production\. Join our community of over 9,000 members as we learn best practices, methods, and principles for putting ML models into production environments\.Why MLOps? MLOps World will help you put machine learning models into production environments; responsibly, effectively, and efficiently\. We’ll be covering topics such as Version Management CI/CD Architecture for Model Deployment Pipeline Scheduling Optimizations Feature Engineering Feature Store Design and Maintenance Effective Data/Machine Learning Strategies New Research And more\!Come share your stories and join us June 14\-17thCreated in collaboration with MLOps Community\.

Generated by Yoast SEO v27.8, this is an llms.txt file, meant for consumption by LLMs.

## Pages
- [Home](https://mlopsworld.com/)
- [FAQs](https://mlopsworld.com/faqs/)
- [CFS Topics](https://mlopsworld.com/cfs-topics/)
- [Call For Speakers](https://mlopsworld.com/call-for-speakers/)
- [Sponsor](https://mlopsworld.com/sponsor/)

## Posts
- [Models are plateauing, Capabilities are becoming a commodity \- What should you do about it? \- Copy](https://mlopsworld.com/post/models-are-plateauing-capabilities-are-becoming-a-commodity-what-should-you-do-about-it-copy/): As frontier AI labs remove key control levers like fine\-tuning, logprobs, and third\-party harness access, practitioners are being pushed toward more closed, vendor\-controlled workflows\. This essay argues that the real advantage of open\-weight models is no longer just cost or capability, it is control: the ability to inspect, customize, and deploy AI systems on your own terms\.
- [Models are plateauing, Capabilities are becoming a commodity \- What should you do about it?](https://mlopsworld.com/post/models-are-plateauing-capabilities-are-becoming-a-commodity-what-should-you-do-about-it/): Frontier AI models are improving, but the bigger pattern may be commoditization, margin pressure, and shrinking practitioner control\. This essay argues that teams building production AI systems should prioritize optionality: portable infrastructure, provider flexibility, workload\-specific evals, and confidence signals they can actually control\.
- [Your AI Stack’s Attack Surface: What to Lock Down Now](https://mlopsworld.com/post/your-ai-stacks-attack-surface-what-to-lock-down-now/): Your AI Stack’s Attack Surface: What to Lock Down Now

This TMLS Insights piece by Graham Toppin examines why AI security risks extend far beyond prompts and models\. Using recent incidents involving LiteLLM and a production database deleted through an agent tool call, the post breaks down practical steps teams can take this week to reduce risk across dependencies, credentials, tool access, and broader AI system assessment\. 
- [Context Rot Is Real\. Here’s How Practitioners Are Managing It\.](https://mlopsworld.com/post/context-rot-is-real-heres-how-practitioners-are-managing-it/): Context Rot Is Real\. Here’s How Practitioners Are Managing It\.

This TMLS Insights piece by Graham Toppin explores “context rot,” the quality degradation that happens when agent sessions accumulate too much competing information\. The post explains why bigger context windows are not enough and outlines practical ways teams can manage context today, including externalizing state, compressing session history, monitoring token usage, and scoping agent tasks more tightly\. 
- [A Quick Guide to Your Local/Open\-Weight Model Setup: A Concrete Starting Point](https://mlopsworld.com/post/a-quick-guide-to-your-local-open-weight-model-setup-a-concrete-starting-point/): TMLS Insights \| Week of May 4, 2026

The first post in a new practitioner\-focused series by Graham Toppin explores why the conversation around local and open\-weight models has shifted from capability debates to operational resilience, sovereign data, and cost control\. This piece breaks down the current pricing landscape, where open\-weight models are closing the gap on frontier systems for coding, reasoning, and constrained production tasks, while still requiring careful workload\-specific testing\. 

## Videos
- [LLMOps Infrastructure for Production\-Grade Agentic RAG Applications with Union\.ai](https://mlopsworld.com/post/video/llmops-infrastructure-for-production-grade-agentic-rag-applications-with-union-ai/)
- [The Building Blocks of Full Stack Agentic Applications](https://mlopsworld.com/post/video/the-building-blocks-of-full-stack-agentic-applications/)
- [Revolutionizing Pharma Commercial with Scalable AI](https://mlopsworld.com/post/video/revolutionizing-pharma-commercial-with-scalable-ai/)
- [Leveraging Cost\-effective GenAI to Enable Compliance while Boosting Efficiency](https://mlopsworld.com/post/video/leveraging-cost-effective-genai-to-enable-compliance-while-boosting-efficiency/)
- [Dynamic Models: Testing, Governance and Implementation](https://mlopsworld.com/post/video/dynamic-models-testing-governance-and-implementation/)

## Categories
- [Videos](https://mlopsworld.com/post/category/videos/)
- [MLOps World \| GenAI Summit 2024](https://mlopsworld.com/post/category/videos/mlops-world-genai-summit-2024/)
- [TMLS 2025](https://mlopsworld.com/post/category/videos/tmls-2025/)
- [Stack Sessions](https://mlopsworld.com/post/category/videos/stack-sessions/)
- [Community Offers](https://mlopsworld.com/post/category/offers/)

## Optional
- [Sitemap index](https://mlopsworld.com/sitemap_index.xml)