GenAI Use Cases and the Road Ahead
Overview
In this episode of the Data Bytes Podcast, Mikiko Bazeley, an MLOps and AI Engineer at Labelbox, shares her insights on the critical role of data quality in generative AI. With a rich background that spans roles at companies like Featureform, NVIDIA, and Intuit, Mikiko emphasizes how high-quality data underpins successful AI models, offering strategies to maintain this quality and discussing the importance of accurate labeling in the AI development process. She also delves into her personal journey across various roles in the tech industry, offering valuable advice for those looking to navigate a career in AI and MLOps. Whether you're interested in the technical aspects of AI or seeking career guidance, this episode is packed with actionable insights and practical advice
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About Mikiko:
Mikiko Bazeley is currently Head of AI Developer Relations at Labelbox. Her main goals are to help: data scientists deploy better models faster; ML platform engineers develop robust & scalable ML systems & stacks without breaking the bank; & bring the delight back into building ML products. Previously, she was Head of MLOps at Featureform. Mikiko has worked as an engineer, data scientist, and data analyst for companies like Mailchimp (Intuit), Teladoc, Sunrun, Autodesk as well as a handful of early stage startups. Mikiko leverages her knowledge and experiences as a practitioner, mentor, and strategist to contribute MLOps & production ML content through LinkedIn, Youtube, & Substack, as well as partnering with companies in the ML ecosystem to build next-gen Data and AI platforms.
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