Josh Funk is a distinguished figure in the data science and MLOps community, celebrated for his practical approach to machine learning and his ability to articulate complex concepts clearly. With over a decade of experience, he has a strong background in developing, deploying, and managing machine learning systems at scale. Josh is passionate about bridging the gap between data science and engineering, advocating for robust MLOps practices to ensure that ML models deliver real business value. He is a prolific writer and speaker, frequently sharing his insights on topics like model deployment, monitoring, data engineering, and the practical application of AI.
Josh Funk's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Recognized for his significant contributions to the MLOps space through widely-read articles, blog posts, and conference talks, helping to shape best practices in the field.
Authored numerous insightful articles and guides on data science, Python, and MLOps, making complex topics accessible to a broad audience of practitioners and learners.
Regularly invited to speak at major industry conferences and events, sharing his expertise on operationalizing machine learning and building effective data products.
Known for advocating and demonstrating how to move AI projects from experimental stages to production-ready, reliable systems that drive tangible outcomes.
UC San Diego
Carnegie Mellon University
Punahou School
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