Beth Zeranski
Global Director Partner Tech Strategist
Waltham, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolBeth Zeranski is a Global Director and partner tech strategist with over 20 years of cross-disciplinary experience—spanning hardware, software, AI, MLOps and DevOps—and a decade-plus focused on cloud and machine learning at Microsoft. She advises CxOs on translating Responsible AI and ML investments into measurable business outcomes while leading technical implementation and partner enablement at scale. A hands-on experimenter, she built AzureML DevOps patterns (including pytest-to-AzureML pipelines) and contributed MLOps work to popular Microsoft repos like Recommenders and computer vision recipes. Known for turning complex technical research from Microsoft Research into practical artifacts for production, she blends rigorous engineering, product leadership, and a knack for mentoring teams. Based in Waltham, MA, Beth’s uncommon background in both firmware/hardware and cloud ML gives her a rare systems-level perspective on AI-driven transformation.
10 years of coding experience
35 years of employment as a software developer
MSCSE Computer Science & Engineering, MSCSE Computer Science & Engineering at University of WashingtonBusiness Analytics, Business Analytics at BU Corp Ed GroupBSEE Electrical Engineering, BSEE Electrical Engineering at Northeastern University
Github Skills (16)
python10
devops10
azure-machine-learning10
cicd10
dockers9
pytest9
kubernetes-pods9
docker9
kubernetes9
machine-learning8
microsoft-azure7
computer-vision7
bash7
azure7
artificial-intelligence6
Programming languages (6)
Github contributions (5)
microsoft/computervision-recipes
May 2019 - Sep 2019
Best Practices, code samples, and documentation for Computer Vision.
Role in this project:
DevOps Engineer & MLOps Engineer
DevOps Engineer & MLOps EngineerContributions:14 commits, 1 PR, 1 push in 3 months
Contributions summary:Beth primarily focused on setting up and configuring the AzureML pipeline for running tests on the computer vision project. They implemented and modified the `submit_azureml_pytest.py` script, incorporating changes for cluster configuration, environment setup, and test execution. Their contributions included adapting the pipeline for running unit tests, integrating the project with Azure services, and ensuring the tests could run effectively on the AzureML platform.
computer-visionmachine-learningdeep-learningpythonjupyter-notebook
liukangling/recommenders
Jun 2019 - Jun 2019
Best Practices on Recommendation Systems
Contributions:79 commits in 10 days
recommendation-system
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