The Ray team at Anyscale recently transitioned to weekly releases to keep up with the rapid pace of innovations in AI models, accelerators, and techniques. Prior to this change, they followed a six-week release cycle that introduced noise into determining root stability and reliability issues due to flaky tests, heavy lifting for releases, and customer frustration. To address these issues, the team tackled instability within Ray's codebase by addressing test failures, systematic updates, and cultural shifts. The transition has resulted in enhanced stability with approximately 350+ issues fixed, streamlined releases, improved productivity, but also presents challenges such as frequent upgrades and API stability. To address these challenges, the team is exploring ideas like a Long-Term Support version and managed upgrade services to provide users with a stable foundation and smooth transitions.