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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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.
Effective technology adoption in regulated industries demands careful consideration of legacy systems' influence, balancing innovation with stability to drive business value.
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.
The Path from One Model to Many
The journey of MLOps can be simplified as progressing from one operational model to multiple models, referred to as scaling from N to N+1. This involves not only deploying the initial model but also developing a robust strategy for managing multiple models in production simultaneously. Companies typically find that as more models are deployed, the operational complexity grows, often requiring new processes and oversight to ensure performance and reliability. This concept illustrates the escalating demands on infrastructure and processes as organizations mature their MLOps capabilities.
Navigating the Tooling Landscape
A significant shift in MLOps involves the excessive focus on tools rather than processes, leading to what is termed 'silver bullet thinking.' Many teams mistakenly believe that acquiring the right tool will solve all their operational problems without addressing foundational processes and culture. Instead, success relies on having a well-defined mission, cultivating a productive team culture, and emphasizing the importance of good processes. By leveraging open-source solutions effectively, teams can often deliver substantial value before investing in commercial tools.
Understanding the Role of Legacy Systems
In heavily regulated industries like banking, the presence of legacy systems significantly influences decisions about technology adoption and innovation. The balance between maintaining stability and pursuing modernization is critical, leading to a cautious approach towards New technologies. Legacy systems can be robust and reliable, making it crucial to find a clear rationale for overhauling or replacing them. This realization often tempers the rush to adopt new tools or platforms, emphasizing the importance of taking measured steps toward innovation.
Aligning Technology with Business Value
To drive meaningful change, organizations must keep a clear focus on how technology investments translate to business value, often requiring constant reevaluation of metrics and objectives. Great leadership embodies the role of aligning team outputs with overall goals, fostering an environment where everyone understands their contribution to the mission. Teams that excel routinely analyze their ROI, tracking the value generated against the effort expended. This not only helps prioritize features and projects but also encourages a mindset focused on sustainability and practical outcomes.
Design and Development Principles for LLMOps // MLOps Podcast #254 with Andy McMahon, Director - Principal AI Engineer at Barclays Bank.
A huge thank you to SAS for their generous support!
// Abstract
As we move from MLOps to LLMOps we need to double down on some fundamental software engineering practices, as well as augment and add to these with some new techniques. In this case, let's talk about this!
// Bio
Andy is a Principal AI Engineer, working in the new AI Center of Excellence at Barclays Bank. Previously he was Head of MLOps for NatWest Group, where he led their MLOps Centre of Excellence and helped build out their MLOps platform and processes across the bank. Andy is also the author of Machine Learning Engineering with Python, a hands-on technical book published by Packt.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
Andy's book - https://packt.link/w3JKL
Andy's Medium - https://medium.com/@andrewpmcmahon629
SAS: https://www.sas.com/en_us/home.html
SAS® Decision Builder: https://www.sas.com/en_us/offers/23q4/microsoft-fabric.html
Data Engineering for AI/ML Conference: https://home.mlops.community/home/events/dataengforai
Harnessing MLOps in Finance // Michelle Marie Conway // MLOps Podcast Coffee #174: https://youtu.be/nIEld_Q6L-0The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses book by Eric Ries: https://www.amazon.co.jp/-/en/Eric-Ries/dp/0307887898
--------------- ✌️Connect With Us ✌️ -------------
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Catch all episodes, blogs, newsletters, and more: https://mlops.community/
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Andy on LinkedIn: https://www.linkedin.com/in/andrew-p-mcmahon/
Timestamps:
[00:00] Andy's preferred coffee
[00:09] Takeaways
[02:04] Andy's book as an Oxford curriculum
[06:13] Register for the Data Engineering for AI/ML Conference now!
[07:04] The Life Cycle of AI Executives Course
[09:55] MLOps as a term
[11:53] Tooling vs Process Culture
[15:01] Open source benefits
[17:15] End goal flexibility
[20:06] Hybrid Cloud Strategy Overview
[21:11] ROI for tool upgrades
[25:41] Long-term projects comparison
[29:02 - 30:48] SAS Ad
[30:49] AI and ML Integration
[35:40] Hybrid AI Integration Insights
[42:18] Tech trends vs Practicality
[44:39] Gen AI Tooling Debate
[51:57] Vanity metrics overview
[55:22] Tech business alignment strategy
[58:45] Aligning teams for ROI
[1:01:35] Communication mission effectively
[1:03:45] Enablement metrics
[1:06:38] Prioritizing use cases
[1:09:47] Wrap up
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