Akshaya Annavajhala
Principal Architect at Microsoft Azure Machine Learning
Bellevue, Washington, United States
Summary
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Rockstar🎓
Top SchoolAkshaya Annavajhala is a Principal Architect at Microsoft Azure Machine Learning with over a decade of experience designing and delivering cloud-native AI platforms. Based in Bellevue, WA, she blends deep systems engineering with platform strategy to help productionize ML workflows at enterprise scale. Her hands-on background includes back-end and DevOps work—notably contributing cross-platform server support to the widely used mlflow project by adapting MLflow’s server to run on Windows via waitress and refining platform-specific dependencies. Earlier in her career she optimized in-memory data models for financial systems at Eagle Investment Systems, giving her a strong performance and data-centric foundation. She holds a BSE in Electrical Engineering from Case Western Reserve University and is known for bridging low-level engineering detail with product-level architecture. Outside the obvious cloud credentials, she’s skilled at making complex cross-platform integrations feel seamless for end users and operators.
9 years of coding experience
1 year of employment as a software developer
BSE, Electrical Engineering, BSE, Electrical Engineering at Case Western Reserve UniversityActon Boxborough Regional High School
Telugu
Github Skills (10)
gunicorn10
mlflow10
windows-server10
python10
devops9
flask8
flask-ask8
machine-learning4
apache-spark4
ml4
Programming languages (14)
Github contributions (5)
mlflow/mlflow
Jun 2019 - Apr 2022
Open source platform for the machine learning lifecycle
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:33 reviews, 4 commits, 7 PRs in 2 years 9 months
Contributions summary:Akshaya's contributions primarily focused on integrating the waitress web server for Windows platforms within the MLflow server. They made changes to `mlflow/cli.py` to validate server arguments, and adjusted the `mlflow/server/__init__.py` to build and run the waitress command. In addition, the user refactored the `setup.py` file to specify platform-specific dependencies for gunicorn and waitress, improving the server's compatibility across different operating systems. Further contributions were directed towards adapting the server code to handle specific Windows environments, effectively bridging the gap between platform specific configurations.
pythonlifecyclemlmachine-learningincremental-learning
akshaya-a/mlflow
Apr 2019 - Sep 2023
Open source platform for the machine learning lifecycle
Contributions:21 pushes, 5 branches in 4 years 6 months
pythonlifecyclemachine-learningdvcmachine-learning-lifecycle
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