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.
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
Instead of better understanding and optimizing their machine learning models, data scientists spend a majority of their time training and iterating through different models even in cases where there the data is reliable and clean. Important aspects of creating an ML model include (but are not limited to) data preparation, feature engineering, identifying the correct models, training (and continuing to train) and optimizing their models. This process can be (and often is) laborious and time-consuming.
In this session, we will explore this process and then show how the AutoML toolkit (from Databricks Labs) can significantly simplify and optimize machine learning. We will demonstrate all of this financial loan risk data with code snippets and notebooks that will be free to download.