Re-Platforming Your Tech Stack // MLOps Podcast #281 with Michelle Marie Conway, Lead Data Scientist at Lloyds Banking Group and Andrew Baker, Data Science Delivery Lead at Lloyds Banking Group.

// Abstract

Lloyds Banking Group is on a mission to embrace the power of cloud and unlock the opportunities that it provides. Andrew, Michelle, and their MLOps team have been on a journey over the last 12 months to take their portfolio of circa 10 Machine Learning models in production and migrate them from an on-prem solution to a cloud-based environment. During the podcast, Michelle and Andrew share their reflections as well as some dos (and don’ts!) of managing the migration of an established portfolio.

// Bio

Michelle Marie ConwayMichelle is a Lead Data Scientist in the high-performance data science team at Lloyds Banking Group. With deep expertise in managing production-level Python code and machine learning models, she has worked alongside fellow senior manager Andrew to drive the bank's transition to the Google Cloud Platform. Together, they have played a pivotal role in modernising the ML portfolio in collaboration with a remarkable ML Ops team.

Originally from Ireland and now based in London, Michelle blends her technical expertise with a love for the arts. Andrew Baker graduated from the University of Birmingham with a first-class honours degree in Mathematics and Music with a Year in Computer Science and joined Lloyds Banking Group on their Retail graduate scheme in 2015.

Since 2021, Andrew has worked in the world of data, firstly in shaping the Retail data strategy and most recently as a Data Science Delivery Lead, growing and managing a team of Data Scientists and Machine Learning Engineers. He has built a high-performing team responsible for building and maintaining ML models in production for the Consumer Lending division of the bank.

Andrew is motivated by the role that data science and ML can play in transforming the business and its processes, and is focused on balancing the power of ML with the need for simplicity and explainability that enables business users to engage with the opportunities that exist in this space and the demands of a highly regulated environment.

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// Related LinksWebsite: https://www.michelleconway.co.uk/https://www.linkedin.com/pulse/artificial-intelligence-just-when-data-science-answer-andrew-baker-hfdge/https://www.linkedin.com/pulse/artificial-intelligence-conundrum-generative-ai-andrew-baker-qla7e/

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Connect with Andrew on LinkedIn: https://www.linkedin.com/in/andrew-baker-90952289

Timestamps:

[00:00] Michelle and Andrew's preferred coffee

[00:25] Takeaways

[01:03] Please like, share, leave a review, and subscribe to our MLOps channels!

[01:26] 5B Cloud Investment!

[04:54] MLOps end-to-end process

[06:21] ML model handoff evolution

[09:14] ML project finalization questions

[14:15] Cloud migration journey

[17:48] Cloud Flexibility vs Rigidity

[19:18] On-prem to Cloud Transition

[23:02] Vertex AI vs Pointed Solutions

[27:47] Standardizing Model Documentation

[30:17] Cloud Optimization and Efficiency

[35:47] Tech debt challenges overcome

[42:11] Chaos engineering insights

[46:11] MLOps platform team collaboration

[51:02] Wrap up