Using Temporal for ML Algorithm Orchestration between Dev and Research

more info at https://robotpaper.ai/zero-touch-algorithm-handoffs-how-we-ship-ml-algorithms-to-prod-without-rewrites-or-ml-engineers/ by Roy Osherove In this video I break down how to ship machine learning algorithms to production without rewrites or ML engineers by using a zero-touch deployment pattern built on Temporal workflows. Traditional ML handoffs—where research code is re-implemented in production apps or sits in backlog with ML engineers—are slow, error-prone, and duplicate effort. Instead, we treat algorithms as Temporal activities owned by researchers and let developers own simple workflows that call those activities, giving us automatic production updates, resilience, scale, and decoupled ownership. This zero-touch algorithm deployment pattern eliminates integration friction, accelerates delivery, improves autonomy between teams, and brings MLOps automation to real researcher-to-prod handoffs without manual intervention