Audrey Smith Joins Thoughts Leaders in Podcast Series


Santa Clara – Audrey Smith, COO of MLtwist, has participated in several podcast appearances throughout the year 2022: 

Most recently in December 2022, Audrey made an appearance on the Alldus podcast where she went deeper into what Data Operations roles entail. Audrey expanded on managing different AI verticals, ML projects, data types, and partners. She also expanded on her career in Data Ops from working an entry level role in machine learning to her Director role and eventually becoming COO at MLtwist.

In July 2023 Audrey participated in a TWIML thought leadership panel along with Adrien Gaidon from Toyota Research, Charlene Chambliss from Aquarium Learning, Janice Tse, Senior Director, Data Science, PayPal and Sam Charrington hosting. The panel discussed how data scientists and ML/AI practitioners can use data-centric AI to make real-world models effective and scalable. They also focused on how by adopting a data-centric approach, industries not typically synonymous with AI-driven applications can leverage AI to drive innovation because of excellent data collection, labeling, and transformation.

In June 2022, Audrey made an appearance on The TWIML AI Podcast with Sam Charrington to discuss data labeling operations for artificial intelligence. This conversation focused on doing a deep dive into data labeling for ML, exploring the typical journey for an organization to get started with labeling, Audrey’s experience when making decisions around in-house vs outsourced labeling, and what commitments need to be made to achieve high-quality labels. Sam and Audrey also discussed how organizations that have made significant investments in data operations typically function, how someone working on an in-house labeling team approaches new projects, the ethical considerations that need to be taken for remote labeling workforces and much more.

Data Operations has played a crucial role in the advancement of artificial intelligence and has enabled the development of more accurate and sophisticated machine learning models. These are must-listen episodes for anyone interested in understanding the inner workings of AI and the critical role that data labeling plays in the scientific advancement of artificial intelligence.


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