Productionalizing Models through CI/CD Design MLflow - Databricks

Productionalizing Models through CI/CD Design MLflow

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



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About Mary Grace Moesta

Databricks

Mary Grace Moesta is currently a Customer Success Engineer at Databricks working with our commercial and mid market customers. As a former data scientist, she worked with Apache Spark on projects focused on machine learning and statistical inference specifically in the retail / CPG space. With previous research in Markov Chain modeling and infectious disease modeling, she enjoys applying mathematics to real work problems.

About Peter Tamisin

Databricks

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.