Wenfei Yan
Software Engineer at Databricks
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolWenfei Yan is a software engineer based in San Francisco with four years of experience building scalable data and ML infrastructure. Currently at Databricks, Wenfei focuses on productionizing ML pipelines and resolving complex dependency and build issues—work reflected in contributions to the MLeap project for deploying ML pipelines and publishing custom releases to Databricks’ S3-backed repositories. Previous roles at AWS and the University of Michigan combined backend engineering, cloud-native architecture (DynamoDB, Lambda, CloudFormation), and applied data science, including a published study on learner engagement. Trained at Carnegie Mellon and the University of Michigan, Wenfei blends rigorous academic research with hands-on MLOps practice and a knack for reducing operational burden in production systems.
4 years of coding experience
1 year of employment as a software developer
Bachelor of Science in Engineering, Data Science Engineering, Bachelor of Science in Engineering, Data Science Engineering at University of MichiganBachelor of Science in Engineering, Electrical and Computer Engineering, Bachelor of Science in Engineering, Electrical and Computer Engineering at UM-SJTU Joint Institute, Shanghai Jiao Tong UniversityMaster of Computational Data Science, Computer Science, Master of Computational Data Science, Computer Science at Carnegie Mellon University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (9)
sbt10
spark10
mlops10
scala10
data-pipelines9
cicd9
data-pipeline9
maven8
aws7
Programming languages (4)
Github contributions (5)
combust/mleap
Sep 2021 - Oct 2021
MLeap: Deploy ML Pipelines to Production
Role in this project:
MLOps Engineer
MLOps EngineerContributions:10 commits, 1 PR, 1 comment in 22 days
Contributions summary:Wenfei primarily focused on modifying the build and deployment process for the MLeap project. Their contributions involved publishing custom releases to a Databricks repository on S3, updating build configurations, and shading/renaming classes to avoid dependency conflicts, especially related to Spark and Scala versions. They also worked on updating the MLeap version hash and merging branches for deployment.
transformerspythonml-pipelinesmleapdata-science
wenfeiy-db/mlflow
Jun 2022 - Oct 2023
Open source platform for the machine learning lifecycle
Contributions:27 pushes, 6 branches in 1 year 4 months
machine-learning-lifecyclepythonlifecyclemachine-learningmlops
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