Chris Samiullah
VP Of Partnerships at Pydantic
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolChris Samiullah is a VP of Partnerships and seasoned tech leader with 11 years designing and shipping production ML and cloud-native systems across healthcare and fintech. He combines hands-on MLOps expertise—deploying, testing and monitoring ML models with tooling like Prometheus, Grafana, Docker and Kubernetes—with strategic program leadership and partnerships at Pydantic. A maker and course author, Chris has published practical repositories and course code demonstrating end-to-end model deployment, validation and versioning workflows. His background spans technical leadership roles at Babylon, Zopa and Bupa China, and an unusual blend of humanities and business training that informs his strength in translating complex technical problems into teachable, product-ready solutions.
11 years of coding experience
11 years of employment as a software developer
MSc. International Business and Management, MSc. International Business and Management at Alliance Manchester Business SchoolB.A. English Literature, B.A. English Literature at University of York
Spanish, French, Chinese
Github Skills (28)
continuous-deployment10
unit-testing10
python10
apidoc10
scikit10
testing10
machine-learning10
grafana10
ml-deployment10
data-preprocessing10
prometheus10
api10
scikit-learn10
test-integration10
test-unit10
Programming languages (8)
Github contributions (5)
trainindata/deploying-machine-learning-models
Jan 2019 - Jan 2023
Code for the online course "Deployment of Machine Learning Models"
Role in this project:
ML Engineer
ML EngineerContributions:3 releases, 41 reviews, 105 commits in 4 years
Contributions summary:Chris contributed to the development of a machine-learning model deployment project, specifically focusing on model pipelines and prediction functionalities. Their commits involve the creation of preprocessors, and configuration files, and the integration of the model with a prediction API. They established data validation processes and implemented versioning for model management, demonstrating a focus on the end-to-end machine learning lifecycle.
pythondata-sciencedeploymentmachine-learningonline-course
trainindata/testing-and-monitoring-ml-deployments
Nov 2019 - Apr 2022
Example project for the course "Testing & Monitoring Machine Learning Model Deployments"
Role in this project:MLOps Engineer
MLOps EngineerContributions:7 reviews, 153 commits, 66 PRs in 2 years 5 months
Contributions summary:Chris's commits primarily revolve around setting up and integrating unit tests, input validation, and monitoring within a machine learning deployment project. They are responsible for creating notebooks to unit test ML code, and creating unit tests for the preprocessing steps and also testing the pipeline itself. The user also implemented shadow mode ML code using threads and included a database population script, suggesting a focus on deploying and monitoring the model. They integrated Prometheus metrics and built dashboards in Grafana, and integrated logging, displaying a complete focus on the MLOps life cycle.
deploymenttestingmachine-learningkubernetesdeployments
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