Navigating the ML Pipeline Jungle with MLflow: Notes from the Field

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Plumbing has been a key focus of modern software engineering, with our API/services/containers/devops driven landscape so it may come as a surprise that plumbing is where AI projects tend to fail. But it is precisely because our modern software development focuses on decoupled plumbing that we have struggled to handle the rise of AI.

Specifically, companies are able to use AI effectively when they are able to create end-to-end AI model factories that explicitly account for coupling between data, models, and code.

In this talk, I will be walking through what a model factory is and how MLFlow’s design supports the creation of end-to-end model factories as well as sharing best practices I’ve observed helping customers from startups to Fortune 50s create, productionize, and scale end-to-end ML pipelines, and watching those pipelines produce serious, game changing business impact.

Session hashtag: #SAISDS11

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About Thunder Shiviah

Databricks Solutions Architect and ex-McKinsey Machine Learning Engineer focused on productionizing machine learning at scale.