Cindy Weng
Deployment Strategist
London, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolCindy Weng is a Deployment Strategist at Databricks with a decade of experience turning data and AI initiatives into measurable business value across enterprise and startup environments. Her background spans senior technical roles at Microsoft and leadership of Data & AI at Traydstream, coupling cloud architecture and MLOps expertise with hands-on improvements to production ML pipelines (notably contributing fixes to Azure MLOps solution accelerators). She blends research roots from Northwestern and IBM with product and analytics experience from HelloFresh and Deloitte, enabling pragmatic, scalable deployments that balance innovation and risk. Based in London, she champions women in tech with humility and a practical focus on outcomes rather than hype.
10 years of coding experience
12 years of employment as a software developer
BSc, Information Systems, BSc, Information Systems at University of Maryland - Robert H. Smith School of BusinessExchange program, Information Systems, Exchange program, Information Systems at Copenhagen Business SchoolMA, Media, Technology, and Society, MA, Media, Technology, and Society at Northwestern University
Chinese, French
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (11)
mlops10
python10
devops9
bash8
machine-learning8
microsoft-azure7
azure7
dockers5
docker5
kubernetes4
kubernetes-pods4
Programming languages (8)
Github contributions (5)
Azure/mlops-v2
Mar 2022 - Dec 2022
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
Role in this project:
MLOps Engineer
MLOps EngineerContributions:12 reviews, 21 commits, 16 PRs in 8 months
Contributions summary:Cindy contributed to the MLOps workflow by fixing model paths and updating evaluation scripts. They made changes to the `sparse_checkout.sh` file, likely related to the project setup or environment configuration. Furthermore, the user addressed an issue in the evaluation by correcting the `y_test` variable. Their work primarily focused on refining existing components and adjusting the project's build and deployment processes.
devopsdeep-learningmachine-learningmlopsazure-machine-learning
Azure/mlops-templates
Apr 2022 - Nov 2024
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
Contributions:17 reviews, 34 PRs, 107 pushes in 2 years 7 months
azureazuremldeep-learningmachine-learningmicrosoft
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