PyData London 2023

The Future of MLOps: Embedding Active Learning into Your ML Model Development Pipelines
06-03, 16:30–17:10 (Europe/London), Minories

Join us for an insightful session on active learning and its applications in machine learning. You will learn how leading teams are embedding active learning into their ML pipelines and how to build your first active learning loop. This session is for ML engineers and data scientists (aspiring or practitioners) who want to stay updated on the latest techniques and learn how to implement active learning with open-source tools.

With the progress in semi-supervised learning and the explosion of generative AI, more AI models are starting to operate in the real world.

This rise of companies with hundreds or thousands of models in production will be accompanied by a fundamental change in the role and structure of machine learning and data science teams. The MLOps industry and preferred stack will transform and with it will come a boom in new use cases, modalities, and world-defining problems that can be solved.

Such a shift will necessitate a stronger focus on new methodologies and initiatives, such as active learning, that will enable teams to reduce the cost and time needed to improve their training dataset and model performance faster and more reliably.

Attendees will gain a deeper understanding of how leading ML and data science teams across industries embed active learning into their ML pipelines. The session is best suited for those with previous experience building, evaluating, and deploying models, as it requires a foundational knowledge of machine learning models. Attendees will leave with two key takeaways: a clear understanding of active learning and its current state, and the knowledge of how to get started building their first active learning loop using cutting-edge open-source tools.

Prior Knowledge Expected

Previous knowledge expected

Frederik is the Machine Learning Lead at Encord. He has a long background in computer vision and deep learning and has completed a PhD in Explainable Deep Learning and Generative Models at Aarhus University. Before his PhD Frederik studied a MSc in computer science while being a teaching assistant for "Introduction to databases" and "Pervasive computing and Software Architecture" at Aarhus University.