Over the course of his 20+ year career, Pete has fulfilled many roles, including data engineer, web developer, trainer, consultant, customer success engineer and most recently tech lead at Databricks. Based in Atlanta, GA, he has delivered and managed projects of varying sizes across multiple verticals, including utilities, financials, higher education and manufacturing. In early 2018 he joined Databricks, where he specializes on topics related to automation for data pipelines.
Often times model deployment and integration consists of several moving parts that require intricate steps woven together. Automating this pipeline and feedback loop can be incredibly challenging, especially in lieu of varying model development techniques. MLflow and the model registry can act as powerful tools to simply building a robust CI/CD pattern for any given model In this talk we will explore how MLflow- specifically the model registry - can be integrated with continuous integration, continuous development, and continuous deployment tools. We'll walk though an end to end example of designing a CI/CD process for a model deployment and implementing with MLflow and automation tools