Michael W. Sherman
Data Scientist
New York, New York, United States
Summary
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RockstarMichael W. Sherman is a Data Scientist and ML Engineer based in New York with 11 years of experience building data pipelines and production-ready model deployments. He combines practical data engineering skills—ETL, SQL cleanup, and scripting—with ML deployment expertise, having contributed scripts for online prediction and deployment workflows to a well-known Google Cloud professional-services repository. Comfortable working across Python and shell tooling, he focuses on making data preparation repeatable and reliable to accelerate model productionization. Known for attention to detail, he has improved code formatting and operational scripts that bridge research models and scalable cloud deployments.
11 years of coding experience
Github Skills (11)
sh10
script10
shell10
bigquery10
data-pipelines10
data-pipeline10
scripting10
gcp10
python9
sql9
mlops8
Programming languages (7)
Github contributions (5)
GoogleCloudPlatform/professional-services
Dec 2019 - Jul 2020
Common solutions and tools developed by Google Cloud's Professional Services team. This repository and its contents are not an officially supported Google product.
Role in this project:
Data Engineer
Data EngineerContributions:46 commits, 4 PRs, 2 pushes in 6 months
Contributions summary:Michael contributed to the project by modifying and adding scripts related to data pipeline operations. Their work involved altering existing Python scripts (`utils.py`) and shell scripts (`run_pipeline.sh`, `deploy_model.sh`, `undeploy_model.sh`, and `online_predict.sh`) used in the data processing and model deployment workflows. They also added scripts for online prediction. Further contributions include fixing formatting and indentation issues within SQL files (`training_features.sql`), demonstrating a focus on data preparation and potentially ETL tasks.
gcpprofessionalgoogle-cloud-mlgoogledata-stream
michaelwsherman/msba-capstone-2020
Dec 2019 - Jun 2020
Contributions:23 PRs, 77 pushes, 8 branches in 5 months
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