Michal Raška
Software Engineer Lead at H2O.ai
Slovakia
Summary
🤩
Rockstar🎓
Top SchoolMichal Raška is a Software Engineer Lead with 12 years of experience, currently driving backend and DevOps efforts at H2O.ai from Slovakia. He combines hands-on engineering with CI/CD expertise, having refactored Jenkins pipelines to enable Docker-based builds, versioning, and artifact publishing for high-profile open-source projects like H2O-3 and the datatable Python package. His work spans machine learning platform internals—bug fixes, data-type and null-handling, model export—and production delivery automation, reflecting a rare mix of ML engineering and release engineering. Educated with a Master's in Computer Science from Charles University, he’s known for pragmatic fixes that improve developer workflows and for quietly improving cross-branch test and publishing controls in complex repositories.
12 years of coding experience
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Univerzita Komenského v BratislaveMaster's degree, Computer Science, Master's degree, Computer Science at Charles University in Prague
Stackoverflow
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Github Skills (35)
xgboost10
docker10
python10
data-manipulation10
machine-learning10
data-export10
dockers10
exporter10
cicd10
h2o10
amazon-s310
build-automation10
aws-s310
jenkins10
jenkins-ci10
Programming languages (8)
Github contributions (5)
h2oai/h2o-3
Aug 2017 - Jun 2020
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:4 reviews, 618 commits, 822 PRs in 2 years 10 months
Contributions summary:Michal primarily contributed to the H2O-3 machine learning platform, focusing on enhancements and bug fixes related to data manipulation and model export. They implemented a new line when printing H2OFrame in IPython. The user also worked on correcting handling of data types and null values and fixed a bug related to column name retention in the h2o.sub function. They also added unit tests for different scenarios and fixed time based calculations in the python client. They also fixed various bugs related to the XGBoost model export and integration.
xgboostgampythonk-meansautoencoders
h2oai/datatable
Mar 2018 - Mar 2019
Role in this project:DevOps Engineer
DevOps EngineerContributions:21 commits, 29 PRs, 162 pushes in 11 months
Contributions summary:Michal's contributions primarily revolve around building and maintaining the continuous integration and continuous delivery (CI/CD) pipeline for the `datatable` project. They refactored and enhanced the Jenkins pipeline, incorporating features like Docker image builds, versioning, and artifact publishing to S3 and a private registry (harbor.h2o.ai). The user also integrated support for Python 3.7 and added options for controlling tests and S3 publishing from different branches.
data-analysispythonftrltabular-datadimensional
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