Prithvi Kannan
Software Engineer at Harvey
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolPrithvi Kannan is a software engineer in San Francisco specializing in AI-enabled products for legal workflows, currently building at Harvey after a three-year stint at Databricks. With seven years of experience across ML platforms, production ML and backend systems, he contributed notable features to the popular open-source MLflow project—improving run reproducibility, artifact retrieval, and pipeline hyperparameter tuning. His background includes internships at Microsoft, Facebook/Instagram, PayPal and a quantitative developer role at Citadel, giving him a blend of NLP, ML platform and trading-system experience. Comfortable shipping production-grade MLOps and backend services, he brings a pragmatic focus on reproducible ML pipelines and developer ergonomics that helps bridge research and product.
7 years of coding experience
5 years of employment as a software developer
High School, High School at Fremont High SchoolUniversity of California, Los Angeles
English, Spanish
Github Skills (12)
mlflow10
mlops10
python10
command-line9
cli9
machine-learning9
command-line-interface9
apache-spark9
git-repository8
git8
github8
cicd7
Programming languages (4)
Github contributions (5)
mlflow/mlflow
Aug 2022 - Nov 2022
Open source platform for the machine learning lifecycle
Role in this project:
Back-end Developer & MLOps Engineer
Back-end Developer & MLOps EngineerContributions:412 reviews, 59 commits, 454 PRs in 2 months
Contributions summary:Prithvi primarily contributed to the MLflow project by implementing features related to run reproducibility and artifact retrieval for pipelines. Their contributions include adding code to run the "reproduce" steps, clarifying server instructions and adding a CLI flag for artifact retrieval. The user also added functionality for hyperparameter tuning in the train step, added logging, and modified the predict step to handle Spark sessions. These changes enhance the MLflow platform's capabilities for experiment tracking and model deployment in pipelines.
pythonlifecyclemlmachine-learningincremental-learning
cs130-w22/Group-A7
Feb 2022 - Mar 2022
Contributions:16 reviews, 34 PRs, 40 pushes in 13 days
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