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Learn how to integrate MLflow with DSPy to bring comprehensive observability and MLOps capabilities to your generative AI application development workflow in this 22-minute lightning talk. Discover how DSPy's framework for authoring GenAI applications with automatic prompt optimization can be enhanced with MLflow's powerful tracking, monitoring, and productization tools. Explore practical techniques for tracking DSPy module calls, evaluations, and optimizers using MLflow's tracing and autologging capabilities. Master the process of debugging, iterating, and understanding your DSPy workflows through enhanced observability features. Gain insights into deploying DSPy programs end-to-end by leveraging the combined power of both frameworks. Walk through real-world demonstrations that show how this integration simplifies the development lifecycle of generative AI applications, from initial development through production deployment.
