## Overview This case study comes from a presentation by John Rauser, Director of Engineering at Cisco, delivered at a Las Vegas conference in 2023. The talk focused on how large enterprises like Cisco are adapting their operational practices to accommodate the deployment and management of Large Language Models (LLMs) in production environments. While the source material is a conference talk abstract rather than a detailed technical document, it provides valuable insight into the enterprise perspective on LLMOps challenges and approaches. Cisco, as a major technology company with extensive enterprise infrastructure experience, brings a unique perspective to the LLMOps discussion. The company has been launching AI-powered products and has developed internal practices for managing these new types of applications. The talk promises to walk through an end-to-end model for LLMOps, suggesting that Cisco has established a relatively mature framework for this purpose. ## The Problem: Traditional DevOps Falls Short for LLMs The core premise of this case study is that the advent of LLMs has created a fundamental shift in how software development and operations must function. Traditional DevOps and DevSecOps workflows, which have been refined over years of software engineering practice, do not translate directly to LLM-powered applications. Several factors likely contribute to this incompatibility, though the source text does not enumerate them explicitly: - **Non-deterministic outputs**: Unlike traditional software where the same input produces the same output, LLMs can generate varying responses to identical prompts, making testing and validation more complex. - **Model versioning challenges**: Managing and tracking different versions of models, prompts, and fine-tuning datasets adds layers of complexity beyond traditional code versioning. - **Resource requirements**: LLMs often require specialized hardware (GPUs, TPUs) and significant computational resources, changing deployment and scaling considerations. - **Security concerns**: LLMs introduce new attack vectors such as prompt injection, data leakage through model outputs, and potential for generating harmful content. - **Latency and performance characteristics**: LLM inference times can be orders of magnitude higher than traditional API calls, affecting application architecture and user experience. ## The Proposed Solution: An End-to-End LLMOps Model The presentation outlines Cisco's approach to addressing these challenges through a comprehensive LLMOps framework. While specific technical details are not provided in the abstract, the talk covers several key operational areas: ### Continuous Delivery for LLMs Continuous delivery in the LLM context likely involves establishing pipelines that can handle not just code changes but also model updates, prompt modifications, and configuration changes. This requires new tooling and processes that can validate LLM behavior before deployment, potentially including automated evaluation suites that test model outputs against expected behaviors and safety criteria. In enterprise settings like Cisco, this probably includes multiple staging environments where LLM applications can be tested with representative workloads before reaching production. The challenge is ensuring that testing is comprehensive enough to catch issues without being so slow that it impedes the development velocity that modern enterprises require. ### Monitoring LLM Applications Monitoring LLM-powered applications requires thinking beyond traditional metrics like uptime and response time. While these remain important, LLM applications also need monitoring for: - **Output quality**: Tracking whether the model's responses meet quality standards over time, detecting potential drift or degradation. - **Token usage and costs**: LLM API calls (whether to internal or external models) often have costs associated with token consumption, requiring careful tracking. - **Safety and compliance**: Monitoring for outputs that violate content policies or could create legal or reputational risks. - **User satisfaction**: Collecting and analyzing feedback to understand how well the LLM is serving its intended purpose. For a company like Cisco with enterprise customers, monitoring likely also includes audit trails and logging that meet compliance requirements for regulated industries. ### Securing LLMs in Production Security is a particular focus of the talk, reflecting the DevSecOps framing. LLM security encompasses multiple dimensions: - **Input validation**: Protecting against prompt injection attacks where malicious users attempt to manipulate the model's behavior through carefully crafted inputs. - **Output filtering**: Ensuring that model outputs do not leak sensitive information, generate harmful content, or otherwise violate policies. - **Access control**: Managing who can interact with LLM applications and what data they can access through those interactions. - **Data protection**: Ensuring that training data, fine-tuning datasets, and user interactions are appropriately protected. - **Model protection**: Preventing theft or unauthorized access to proprietary models or fine-tuning that represents significant investment. In an enterprise context, these security measures must integrate with existing identity management, network security, and compliance frameworks. ### On-Call Operations for LLM Applications The mention of "go on-call" for LLM applications is particularly interesting as it acknowledges that LLM applications require operational support just like any other production system, but with unique characteristics. On-call engineers for LLM applications need to be prepared for: - **Model behavior issues**: Responding to situations where the model is producing inappropriate, incorrect, or unexpected outputs. - **Performance degradation**: Addressing slowdowns or resource exhaustion that may have different root causes than traditional applications. - **Security incidents**: Responding to attempted attacks or detected vulnerabilities in real-time. - **Integration failures**: Handling issues with the various systems that LLM applications typically connect to for retrieval, data access, or action execution. This requires new runbooks, training, and potentially new tooling to help on-call engineers diagnose and resolve LLM-specific issues. ## Lessons from Recent Product Launches The abstract mentions that the talk draws from "recent product launches involving AI at Cisco." While specific products are not named, this suggests that Cisco has practical, hands-on experience deploying LLM-powered applications and has learned lessons from those experiences. This practical grounding is valuable because it means the proposed LLMOps model is not purely theoretical but has been tested against real-world constraints and challenges. However, it should be noted that the source material is a conference talk abstract, which by its nature is promotional and highlights successes rather than failures or ongoing challenges. A balanced assessment would recognize that while Cisco has made progress in this area, LLMOps remains an evolving discipline and even large enterprises are still learning and adapting their approaches. ## Enterprise Context Considerations The talk specifically addresses "LLMOps in the large enterprise," which brings particular considerations: - **Scale**: Large enterprises may have multiple LLM-powered applications across different business units, requiring coordination and governance. - **Compliance**: Enterprises in regulated industries (many of Cisco's customers are in finance, healthcare, and government) face additional requirements for audit, explainability, and data handling. - **Integration**: LLM applications must work alongside existing enterprise systems, requiring careful attention to APIs, data flows, and authentication. - **Governance**: Enterprises need clear policies and processes for approving, deploying, and retiring LLM applications. ## Critical Assessment While this case study provides a useful high-level framework for thinking about LLMOps in enterprise settings, several limitations should be acknowledged: - **Limited technical detail**: The source material is a talk abstract, not a detailed technical paper, so specific implementation details are not available. - **Promotional nature**: Conference talks are inherently promotional, and this abstract does not discuss challenges, failures, or lessons learned from mistakes. - **Generality**: The end-to-end model mentioned is described in general terms; specific tools, processes, or metrics are not detailed. - **Evolving field**: LLMOps practices are rapidly evolving, and what was state-of-the-art in 2023 may have been superseded by newer approaches. Despite these limitations, this case study is valuable for highlighting the enterprise perspective on LLMOps and for framing the key operational challenges that organizations face when deploying LLMs at scale. The emphasis on security (the "Sec" in DevSecOps) is particularly relevant given the ongoing concerns about LLM safety and the potential for misuse. ## Conclusion Cisco's approach to LLMOps, as outlined in this presentation, represents an attempt to bring the discipline and rigor of enterprise DevSecOps to the new world of LLM-powered applications. By addressing continuous delivery, monitoring, security, and on-call operations, Cisco is working to create a comprehensive framework that can support AI applications in production enterprise environments. While the specifics of their implementation are not detailed in the available source material, the framework itself provides a useful reference point for other organizations facing similar challenges.