The rapid adoption of large language models and multimodal foundation models has made multimodal data preparation pipelines critical AI infrastructure. These pipelines interleave CPU-heavy preprocessing with accelerator-backed (GPU/NPU/TPU) inference and produce massive intermediate artifacts. Achieving high throughput is difficult because workloads are highly non-stationary: regime shifts, input-dependent inference, and transient memory spikes cause rapid performance fluctuations and out-of-memory (OOM) failures. Existing schedulers typically rely on threshold-based autoscaling or assume synchronous, homogeneous operators, leading to poor efficiency. We present Trident, an adaptive scheduling framework for heterogeneous multimodal pipelines on fixed-resource clusters. Trident closes the loop across three coupled layers: (i) an observation layer that estimates per-operator sustainable throughput for asynchronous operators via Gaussian Process regression with anomaly filtering; (ii) an adaptation layer that detects workload shifts online and performs memory-constrained Bayesian optimization to recommend OOM-safe configurations; and (iii) a scheduling layer that solves a mixed-integer linear program to jointly optimize operator parallelism, placement, and configuration transitions under heterogeneous compute and bandwidth constraints, accounting for cold-start overhead via rolling updates. Decisions trigger sample invalidation and model refresh to keep estimates consistent with the active configuration. Implemented on Ray Data, Trident improves end-to-end throughput by up to 2.01x on a document curation (PDF) pipeline and 1.88x on a video curation pipeline over a static baseline, with low overhead suitable for online re-optimization.

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