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mirekphd
  • Member for 1 year, 6 months
  • Last seen more than a week ago
About

In my data scientist hat I work in python on creating ML modeling pipelines, from data munging, feature selection and engineering, model training, performance optimization, reproducibility, to validation and monitoring of post-production model performance.

In my ML Engineer / ML Ops hat I develop in-house Docker containers for data scientists working with ML models development (GPU-enabled, python, R, H2O) with familiar interfaces like Jupyter, RStudio Server, VS Code or H2O Flow, and specialized apps such as ML Flow or generic file management or database tools such as Cloud Commander or ML Upload, minio or mariadb. I also develop REST APIs for the production deployment of these ML models and their features (using python, sometimes Java (for H2O models), Flask, gunicorn, Redis, minio, git).

In my DevOps hat I orchestrate the two types of ML containers (stateful for ML models development and stateless for their production deployment) in several Openshift clusters, automating their builds, packages/libraries/extensions updates, security scans, and staging/production deployments using Jenkins pipelines, bash, python, and Groovy scripts and Openshift build/deployment configs, all integrated using webhooks. I also perform linux system admin role for the CI/CD build server (CentOS, docker, docker-compose, OKD, Jenkins, clair, Postgres) and Openshift business admin role for the data science development and ML models production clusters.

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