As machine learning (ML) moves closer to the data source, edge deployments in resource-constrained environments such as wildlife camera traps face critical operational challenges. Edge devices like the Raspberry Pi have limited compute, memory, storage, and power, where even minor inefficiencies can lead to system failures. Ensuring that images are processed, scored, and acted upon in near real-time requires careful attention to pipeline behavior beyond the ML model itself. This poster presents operational insights from building and testing a camera trap pipeline using the ML Field Planner framework [1][5] on a Raspberry Pi 5. The setup simulated a field-like environment to evaluate end-to-end responsiveness, resource utilization, and reliability, with the goal of identifying bottlenecks and implementing enhancements to make edge ML pipelines deployment-ready. Our testing revealed three key operational factors. First, sequential inference bottlenecks are a major limitation on devices without GPU support, where parallel processing is not feasible. If a single image takes ~10 seconds to score, subsequent images queue up, increasing latency and extending total processing time, which delays downstream actions such as triggering a drone. Second, unregulated image capture without filtering floods the pipeline with blank and redundant frames, straining compute, memory, and storage. Finally, small inefficiencies compound downstream, where queued frames and delays increase the risk of dropped frames or missed actions. To overcome these challenges, we applied operational tuning strategies that made the edge pipeline efficient and field-ready. Time- and motion-based configuration removes redundant frames and prevents queue buildup by triggering events only when meaningful changes occur, such as significant pixel differences or consecutive motion frames. Queue-aware pipeline management throttles input to match inference time, ensuring that only one image is processed until the previous one is scored. This makes the entire pipeline execution bound to model inference time, for example, if inference takes 5 seconds, the full pipeline completes in 5 seconds. Adaptive event sensitivity improves reliability under varying conditions, while resource-aware data retention stores only meaningful frames, reducing storage pressure while preserving critical events. Our study highlights that workflow-specific tuning and accuracy–latency balancing are essential for reliable edge ML. Simulator workflows can tolerate 15–30 seconds per image for higher accuracy, while real-world field deployments require 2–5 seconds to support timely, event-triggered downstream actions such as drone dispatch. These results demonstrate that operational precision is as important as model accuracy—focusing on image flow regulation, adaptive thresholds, and inference-aware control allows small, low-level adjustments to transform fragile pipelines into reliable, field-ready systems, ensuring efficient resource usage, timely event capture, and sustainable ML deployments in resource-constrained environments.