Adam Palmar is a Senior Machine Learning Engineer based in Copenhagen with eight years of experience building and productionizing ML systems, primarily at Unity where he helps data scientists train and deploy thousands of models monthly. He focuses on ML platform work—designing, deploying and monitoring training and tracking infrastructure—and played a key role adopting Ray as a distributed compute stack to scale Unity’s ads and recommendations workloads. Adam bridges research and engineering, having moved recommendation and automated domain randomization projects from prototype to production, and he collaborates closely with data scientists and infra teams to remove bottlenecks. Outside of ML platform engineering, he applies his expertise to game development in the Unity Engine, giving him practical insight into the product domains his models serve.
Repository for makeinga a GitHub Actions for deploying to Kubeflow.
Contributions:1 push in 1 day
deployingkubeflowkubernetes
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