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Design and Development Principles for LLMOps // Andy McMahon // #254

Aug 20, 2024
01:10:17
Snipd AI
Andy McMahon, a Principal AI Engineer at Barclays Bank, shares his expertise on LLMOps principles, highlighting the essential shift from MLOps to managing large language models. He discusses the complexities of AI and machine learning operations, emphasizing automation and testing challenges. Andy reflects on the evolving tech landscape, stressing the importance of aligning technology with business goals and effective communication of ROI. He also notes the vital role of product managers in optimizing AI interactions to create real value for organizations.
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Podcast summary created with Snipd AI

Quick takeaways

  • Transitioning from MLOps to LLMOps requires a commitment to foundational software engineering practices while integrating new techniques for success.
  • Managing multiple models in production amplifies operational complexity, highlighting the need for robust strategies and processes to ensure reliability.

Deep dives

Defining MLOps and Its Lifecycle

MLOps is intricately linked to traditional software development but introduces unique challenges inherent to machine learning. The lifecycle starts with clear requirement scoping, followed by development cycles similar to those in software engineering, including testing and production deployment. However, the complexity arises from the nature of machine learning where data quality plays a crucial role, leading to discussions about 'garbage in, garbage out.' Organizations often discover that existing software processes can be adapted rather than reinvented for ML applications, emphasizing the importance of leveraging established best practices within new contexts.

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