AutoML object detection can be approached by considering several factors that influence performance. If optimizing the current setup doesn't yield the desired efficiency, consider deploying the model for online predictions. Online predictions handle individual requests in real-time, which can be more efficient for smaller datasets or when immediate results are required. However, this approach may not be suitable for large-scale batch processing due to potential scalability constraints.

By systematically addressing these areas, you can enhance the efficiency of your batch predictions in AutoML object detection tasks.