Abstract: In recent years, we have seen amazing results in artificial intelligence and machine learning owing to the emergence of models such as transformers and pretrained language models. Despite the astounding results published in academic papers, there remains a lot of ambiguity and challenges when it comes to deploying these models in industry because: 1) troubleshooting, training, and maintaining these models is very time and cost consuming due to their inherent large sizes and complexities 2) there is not yet enough clarity about when the advantages and challenges of these models outweigh classical ML models. These challenges are even more severe for small and mid-sized companies that do not have access to huge compute resources and infrastructure. In this talk, we discuss these challenges and share our findings and recommendations from working on real world examples at SPINS, a data/tech company focused on the natural grocery industry. More specifically, we describe how we leverage state-of-the-art language models to seamlessly automate parts of SPINS’ data ingestion workflow and drive substantial business outcomes. We provide a walk through of our end-to-end MLOps system and discuss how using the right tools and methods have helped to mitigate some of these challenges. We also share our findings from our experimentation and provide insights on when one should use these massive transformer models instead of classical ML models. Considering that we have a variety of challenges in our use cases from an ill-defined label space to a huge number of classes (~86,000) and massive data imbalance, we believe our findings and recommendations can be applied to most real-world settings. We hope that the learnings from this talk can help you to solve your own problems more effectively and efficiently!
Bio: Elliot Henry is a Data Science Manager at SPINS; he is responsible for the successful execution of data science projects and the overall support and care of his team. He has experience managing projects from the ideation phase through delivery of the final product, and is passionate about building end to end machine learning systems using software based methods. Prior to SPINS, Elliot has experience as a data scientist in the fields of retail, marketing, and digital. He holds a B.A. in Biochemistry from Dartmouth College and a M.S. in Analytics from the University of Chicago. In his free time, he enjoys playing board games with friends.