Abstract: Wherever you are in your team's machine learning journey, it's helpful to think about evolving towards large scale production. A key ingredient of this journey is your data labeling and annotation framework. In this talk we focus on how to build your data labeling pipeline to be enterprise grade. We will describe the considerations and insights that go into making your data pipeline a mindful part of your development pipeline. Proactively planning a data process can generate progressively better results during development, but it requires some thought and stakeholder buy-in.

Abstract: Academics and practitioners alike believe that continual learning (CL) is a fundamental step towards artificial intelligence. Continual learning is the ability of a model to learn continually from a stream of data. In practice, this means supporting the ability of a model to autonomously learn and adapt in production as new data comes in. The idea of CL is to mimic humans ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. CL of models in production will improve accuracy, and bring artificial intelligence one step closer to real human intelligence.