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Would you be better off deploying an ML model or the code that generates the model? This talk, targeted to practitioners, covers different deployment patterns for machine learning applications. Beyond introducing these patterns, we’ll discuss the downstream implications of each with respect to reproducibility, audit tracing, and CI/CD. To demonstrate solution driven architecture, we’ll lean on Delta and MLflow as core technologies to track lineage and manage the deployment strategy. The goal of this session is to empower practitioners to design efficient, automated, and robust machine learning systems.

PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.

PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

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