Michael Sherman
Machine Learning Engineer, Generative AI Solution Architecture, Applied AI Engineering
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolMichael W. Sherman is a Data Scientist and ML Engineer in New York with 10 years of experience building and productionizing data-driven systems. He works across Python, shell scripting, and SQL, contributing to GoogleCloudPlatform/professional-services by adding online prediction scripts and enhancing pipeline and deployment workflows. His practical focus is on operational ML and data engineering—designing ETL processes, orchestrating pipelines, and automating model deploy/undeploy tasks. A detail-oriented collaborator, he even improves maintainability at the code and SQL level (fixing formatting/indentation), reflecting a commitment to reliable, production-ready workflows.
11 years of coding experience
8 years of employment as a software developer
Bachelor of Science, Bachelor of Science at Northwestern UniversityMaster of Science - MS, Master of Science - MS at The University of Texas at Austin - Red McCombs School of Business
English, swati
Stackoverflow
523reputation
68kreached
3answers
11questions
Github Skills (21)
bigquery10
data-pipelines10
scripting10
gcp10
script10
sh10
shell10
data-pipeline10
python9
sql9
mlops8
google-compute-engine6
ggplot26
apply6
data-cleaning6
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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