Anmol Bal
DevOps Engineer at h2o.ai
Mountain View, California, United States
Summary
🤩
RockstarAnmol Bal is a DevOps engineer with 10 years of experience based in Mountain View, California, focused on build, release and CI/CD automation for large open-source ML projects. He has driven infrastructure and pipeline improvements for notable repositories such as H2O (h2o-3) and datatable, adding Hadoop compatibility, robust hashing for release artifacts, and Jenkins/Docker pipeline stages for artifact generation and core dump collection. Comfortable as both release manager and automation engineer, he blends scripting and tooling changes with release integrity measures like SHA256/MD5 handling. Anmol’s work shows a practical knack for hardening release workflows and adapting complex builds to new platform versions—skills that quietly reduce friction for data-science users and downstream distributions.
10 years of coding experience
Github Skills (24)
docker10
bash10
hadoop10
makefile10
dockers10
cicd10
release-management10
build-automation10
jenkins10
jenkins-ci10
github-ci9
python9
groovy9
open-source9
githubaction-workflow9
Programming languages (11)
Github contributions (5)
h2oai/h2o-3
Feb 2016 - Jul 2019
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:
DevOps Engineer & Release Manager
DevOps Engineer & Release ManagerContributions:1 review, 87 commits, 97 PRs in 3 years 5 months
Contributions summary:Anmol's contributions primarily revolve around build and release automation. They focused on updating the `make-dist.sh` script to support new Hadoop versions (including CDH and HDP), add support for HDP 2.6 and CDH5.13, and fix MD5 generation for the h2o.jar file, specifically for CRAN releases. Furthermore, they implemented features to include SHA256 hashes in the buildinfo.json and output text files, enhancing build integrity and distribution processes. The user also reverted commits and fixed tests.
automldeep-learningelastic-netgbmgradient-boosting
h2oai/datatable
Jun 2017 - Mar 2020
A Python package for manipulating 2-dimensional tabular data structures
Role in this project:DevOps Engineer & Automation Engineer
DevOps Engineer & Automation EngineerContributions:24 commits, 31 PRs, 205 pushes in 2 years 9 months
Contributions summary:Anmol primarily focused on improving the CI/CD pipeline and build processes for the datatable project. They made changes to the Jenkinsfile and Dockerfiles, including updates to image tags and build configurations. Contributions also included the implementation of new stages, artifact generation, and core dump collection, demonstrating automation and infrastructure management skills. Further refinements involved environment setup, testing, and incorporating new library versions into the build system.
data-structuredata-structurespythondata-analysisperformance
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