[{"data":1,"prerenderedAt":179},["ShallowReactive",2],{"maintainer-ntkathole":3,"mdc-kyvdaa-key":59,"mdc--odqf2w-key":71,"mdc--r208ds-key":79,"mdc--4vn8o4-key":108,"mdc-n4py86-key":116,"mdc-24sedc-key":131,"mdc-gzbmtn-key":139,"mdc--8qoih0-key":147,"mdc-5gfh53-key":163,"mdc--qvhzhj-key":171},{"id":4,"title":5,"body":6,"created_on":7,"description":6,"designation":8,"extension":9,"form":10,"full_name":35,"meta":36,"navigation":37,"path":38,"photo":39,"projects":40,"projects_list":6,"seo":48,"socials":49,"stem":56,"username":57,"__hash__":58},"maintainers\u002Fmaintainers\u002Fntkathole.json","Ntkathole",null,"2026-05-13T11:16:14.003876+05:30","Principal Software Engineer @ Red Hat","json",[11,14,17,20,23,26,29,32],{"question":12,"response":13},"How to support","- Contribute features, bug fixes, or performance improvements and bench-marking (especially around offline\u002Fonline store integrations, scalability, and infra).\n- Improve documentation.\n- Share feedback from production deployments.\n- Help review PRs (this is very valuable).",{"question":15,"response":16},"A small brief about your project","Feast is an open-source feature store that sits at the center of modern ML systems. It standardizes how features are defined, stored, and served - bridging offline training and online inference. It enables teams to reuse features, ensure consistency between training and serving, and scale ML systems using existing data infrastructure.",{"question":18,"response":19},"One FOSS maintainer lesson for your younger self","**Design for contributors, not just users.** Clean abstractions and good docs matter more than clever code if you want a project to grow.",{"question":21,"response":22},"Why do you do it? 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