LLMOps and Platform Engineering: Who Should Own the AI Pipeline?
AI Executive Summary
The introduction of large language models (LLM) has disrupted the traditional model deployment process, requiring a new set of practices, tools, and workflows known as LLMOps.
This has created a gap in the ownership model, with LLMOps, MLOps, and DevOps competing for the same pipeline.
Platform engineering is infrastructure-centric, while MLOps is model-centric, and LLMOps is a specialized subset of MLOps that focuses on the development, deployment, and management of LLM.
Why It Matters
⚡ Structural ImpactThe emergence of LLMOps highlights the need for a more integrated approach to AI pipeline management, as the traditional siloed approach can lead to a shadow-IT problem. The complexity of LLM requires a more nuanced evaluation of their output, beyond just accuracy, to ensure they are secure and trustworthy.
Multi-Vector Implications
- TECHNICALThe integration of LLMOps with existing MLOps and DevOps tools requires a re-evaluation of infrastructure, access controls, and deployment strategies.
- MARKETThe growth of LLMOps is likely to drive demand for specialized tools and services that can support the development and deployment of LLM.
- GOVERNANCEThe ownership model for AI pipelines must be re-examined to ensure that platform engineering, MLOps, and LLMOps are aligned and working together to prevent shadow-IT problems.
Strategic Outlook
🔭 12-18M HorizonOver the next 12-18 months, we can expect to see increased investment in LLMOps tools and services, as well as a greater emphasis on integrating LLMOps with existing MLOps and DevOps workflows. This will require a more nuanced understanding of the complexities of LLM and the need for specialized practices, tools, and workflows to support their development and deployment.
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LLM
A Large Language Model (LLM) is a type of artificial intelligence model trained on vast amounts of text data to understand, generate, and manipulate natural language. Built on the Transformer architecture, LLMs use billions of parameters to recognize semantic patterns and reasoning relationships.
ARR
ARR (Annual Recurring Revenue) is a key metric for subscription-based businesses representing the predictable recurring revenue generated by active customers over a year.
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