arXiv Machine Learning
Event-Driven ML Pipeline Orchestration for Manufacturing: An AWS Industry Experience
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
Hugging Face Trending Papers
Jul 6
Adaptive Inference Batching using Policy Gradients
Inference serving systems must balance throughput and latency under bursty, heterogeneous workloads, yet the industry standard remains static batching policies that require manual tuning and cannot adapt to shifting traffic. We investigate whether reinforcement learning (RL) can learn adaptive batching and routing policies that outperform these heuristics, training REINFORCE and PPO agents on a discrete-event simulator validated against queuing theory and production traces (Azure Functions, BurstGPT).
arXiv AI
Oct 15
Seamless Deployment of Machine Learning Inference at the Far Edge: An Empirical AWS IoT Greengrass Architecture for Industrial Predictive Maintenance
arXiv:2610.14815v1 Announce Type: cross Abstract: Industrial IoT sensors produce continuous, high-volume data streams that predictive maintenance needs to analyse in real time. Cloud-centric architec...
arXiv AI
Jul 7
Adaptive Inference Batching using Policy Gradients
arXiv:2607. 05272v1 Announce Type: cross Abstract: Inference serving systems must balance throughput and latency under bursty, heterogeneous workloads, yet the industry standard remains static batching policies that require manual tuning and cannot adapt to shifting traffic.
arXiv AI
Jun 2
Deft Scheduling of Dynamic Cloud Workflows with Varying Deadlines via Mixture-of-Experts
arXiv:2606. 01162v1 Announce Type: new Abstract: Workflow scheduling in cloud computing demands the intelligent allocation of dynamically arriving, graph-structured workflows with varying deadlines onto ever-changing virtual machine resources.
arXiv AI
Oct 5
Zephon: Elastic Determinism for Online, Stateful Foundation Model Data Loading Pipelines
Zephon is a data loader designed for foundation model training that ensures deterministic ordering of training data batches even when GPU resources change, checkpoints are resumed, or different processing backends are used. It handles online, stateful pipelines—where tokenization, packing, and mixing of samples create complex n‑to‑m transformations—by partitioning the data stream into topology‑independent lanes, serializing ordering decisions, and checkpointing only bounded in‑flight state. Experiments on text and vision‑language tasks show Zephon delivers competitive throughput while offering guarantees that existing loaders lack for such pipelines.
arXiv Machine Learning
Aug 18
Global Simulation-Guided Dynamic Operator Scheduling for Efficient Multi-Tenant Model Serving
arXiv:2608. 15762v1 Announce Type: cross Abstract: Container-granularity scheduling leaves abundant short-lived idle slices within containers unexploited.