Senior Engineer I at Carvana
Brittney has a vast and varied history within the data realm; including experience as a data analyst, a database administrator, and the last few years, a data engineer. She is incredibly passionate about data governance and validation solutions, and the best way to implement both in tandem within data systems. Brittney is currently deeply interested in learning further best practices around MLOps and how to bridge the gap between data science and data engineering.
Watch live: March 7, 2023 @ 1:30 – 2:00 pm ET
Automated, Scalable and Quality machine learning with Airflow, Kubernetes, and Great Expectations
Previously, the NLP model training was a manual process. These steps included piecemeal jobs spread across multiple GCP projects with various timing/scheduling. Airflow enables us to automate the entire process on a schedule or on-demand with little to no human intervention. We can now break down a monolithic job into several dependent components. This prevents full job failure, allows us to reprocess independently, and train models faster in parallel. We further implemented GKEStartPodOperator to isolate dependencies and spin up customizable resources as needed, as well as incorporated Great Expectations for data quality checks.