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Every day brings news of more advanced AI technologies - from advanced algorithms in adaptive learning and neural networks to hardware optimized libraries for GPUs and TPUs. However, these advances can only benefit businesses when AI solutions are put into production to solve business problems. While AI/ML experiments abound, only a small fraction of these experiments have seen their way to production solutions that generate Return On Investment (ROI) for their businesses.

Companies that have deployed production ML have noted that a combination of ML techniques, classic software engineering best practices, and DevOps methodologies are required for successful production ML. As more businesses seek to benefit from AI/ML, practitioners are developing and sharing techniques, tools, and best practices for Production ML - MLOps.

In this webinar, we cover the state of the art of MLOps. We describe the end to end lifecycle of an AI technology from model training to production deployment and application integration, using state of the art cloud tools. We highlight the unique production challenges generated by ML models and the latest innovations, tools, and practices for monitoring, debugging and managing enterprise production ML. Finally we provide an overview of the emerging regulatory demands on production ML in different industries and geographies.

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