Last month, I attended two great summits organized by REWORK: The MLOps summit that discovers how to optimize the ML lifecycle & streamline ML pipeline for better production and the ML Fairness summit that discovers strategies to ensure ML models are accountable & fair to build secure & responsible AI. As a previous attendee of REWORK’s in-person summit, I have always enjoyed the unique mix of academia and industry, enabling attendees to meet with AI pioneers at the forefront of research and explore real-world case studies to discover the business value of AI.

In this long-form blog recap, I will dissect content from the talks that I found most useful from attending the summit. The post consists of 10 talks that range from automated data labeling and pipeline optimization, to model fairness and responsible AI at scale.