Parallel Orchestration Patterns for ML Workloads in Production
The Multi-Model Problem
Many ML systems start simple. That's the right call to start with as it helps with quick prototyping and building the integrations. The problems start when requirements sharpen and a single general-purpose model stops being the right answer. We want computer vision for frames, NLP for transcripts, audio analysis for the audio track. We want models purpose-built for their domain, not one model doing all three passably. A decision engine should not use the same model for behavioral signals and graph traversal. What started as a single inference step becomes five, then a pipeline pulling signals from three modalities simultaneously, then a decision engine combining outputs from behavioral models, graph models, and rule-based engines. Now we are not orchestrating one model. We are orchestrating a fleet.
The instinctive approach is sequential execution. We call model A, wait for it to finish, call model B, and so on....
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