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This conference talk provides a field guide to implementing cloud native AI/ML systems for both developers and data scientists who may be overwhelmed by the complex ecosystem of tools and practices. Navigate through the ML lifecycle while learning about essential open source tools, DevOps practices, and cloud native infrastructure that support AI/ML implementation. Gain practical insights on how developers can build performant end-to-end AI/ML systems, while data scientists discover how MLOps enables rapid experimentation, model drift detection, and model integrity. The presentation includes take-home labs to help attendees begin their AI/ML deployment journey, making it valuable for busy professionals who need to understand the fundamentals of operationalizing AI/ML in cloud native environments.
