Abstract: As the requirements of ML capabilities within operational decision making increase, our approaches to ML development, deployment and maintenance need to continuously evolve. The causes for business are wide and varied, from ever-increasing complexity of data and pipelines, to the need for lower and lower latencies or the challenges of integrating extremely large in-memory models. MLOps has become the key terminology to underpin the process, practice and tooling to unlock an approach to ML in production. However, what happens when your MLOps needs upgrading? In this talk, Leanne will take us through how the FT, already with a large number of models in production, are spearheading a journey to improve, iterate and upgrade the way they develop, deploy and monitor their ML and Data Science capabilities, all whilst keeping their current capabilities running. Leanne will highlight they key approaches and considerations when looking to improve your MLOps processes, and how you can expedite your ML in production activities, while ensuring you keep “the car on the road”.
Bio: Leanne is Director of Data Science at the Financial Times and is a passionate, experienced data leader having built and developed empowered data science and analytics teams for a variety of businesses; from startups to large organisations. Leanne is in her element when developing and implementing strategic, technical and cultural solutions to getting data & analytical capabilities into the operational ecosystem. She is an active part of the data and technology community, sharing innovation and insights to encourage best practice, from Manchester, UK to Austin, TX and is an Advisory Panel Board Member. Outside of all things data you can ask Leanne about her golf swing (it’s not good - yet), her passion for American Football (specifically the Cincinnati Bengals), her latest sewing project, and her love for good music, food and whisky.
Director of Data Science | Financial Times