Re-Platforming Your Tech Stack // Michelle Marie Conway & Andrew Baker // #281

Key takeaways

A strategic move to the cloud can significantly enhance operational efficiency, as demonstrated by the reduction in model run times from hours to minutes.
Effective communication and collaboration across teams are essential for successful migration and ongoing support of machine learning models.
Continuous upskilling in technical areas is crucial for professionals working at the intersection of data science and engineering.
Prioritizing robustness and explainability in machine learning models over pursuing perfection leads to better long-term outcomes.
Anticipating and being prepared for challenges during a large migration project can help teams navigate adversity more effectively.

Summary

Migrating to the Cloud: A Strategic Shift

Michelle Canway and Andrew Baker share their experiences at Lloyds Banking Group as the organization transitions its portfolio of machine learning models from an on-premises solution to the cloud. This five-year, $3 billion project is aimed at leveraging the capabilities of Google Cloud to enhance operational efficiency and unlock new opportunities. The discussion highlights lessons learned from the migration process, emphasizing the logistical challenges faced in coordinating multiple teams, each with different model requirements.

Team Dynamics and Collaboration

The podcast delves into the importance of collaboration between various teams, including the ML Ops team, local engineers, and risk teams. Andrew and Michelle emphasize the significance of clear communication and understanding each team's needs for ensuring a smooth transition. They also point out the dynamic role that engineers play in the migration process, as they must adapt and refine ‘customer-driven’ solutions to meet user-specific requirements.

Technical Upskilling and Knowledge Transfer

Michelle discusses her journey of technical growth while embedded with the platform engineering team. She highlights the value of stepping out of comfort zones, upskilling in software engineering, and the willingness to ask for help. This experience underlined how crucial it is to build relationships and effectively communicate technical needs between data science and engineering teams.

Robustness over Perfection: The Engineering Perspective

Andrew articulates the philosophy that robustness and explainability in machine learning models often trump the pursuit of perfection. He notes that teams should focus on creating models that are sustainable and reliable over time, instead of striving for the 'perfect' model. The discussion emphasizes the importance of consistent engineering practices in maintaining a diverse portfolio of ML models.

Challenges and Lessons Learned During Migration

The discussion highlights various challenges encountered during the migration, from unexpected changes in Python package compatibility to logistical nightmares of coordinating multiple teams. Andrew and Michelle share anecdotes illustrating the growing pains of transitioning. The conversation reflects on how adapting and being resilient to unforeseen complications is a vital skill in managing large-scale migrations.

Show notes

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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Timestamps:

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