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The ocean remains one of the least explored places on our planet, containing myriad life that are either unknown to science or poorly understood. Given the technological challenges and limited resources available for exploring this vast space, more targeted approaches are required to scale spatiotemporal observations and monitoring of ocean life. The promise of autonomous underwater vehicles to fulfill these needs has largely been hindered by their inability to adapt their behavior in real-time based on what they are observing. To overcome this challenge, we developed Deep Search and Tracking Autonomously with Robotics (DeepSTARia), a class of tracking-by-detection algorithms that integrate machine learning models with imaging and vehicle controllers to enable autonomous underwater vehicles to make targeted visual observations of ocean life. We show that these algorithms enable new, scalable sampling strategies that build on traditional operational modes, permitting more detailed (e.g., sharper imagery, temporal resolution) autonomous observations of underwater concepts without supervision and robust long-duration object tracking to observe animal behavior. This integration is critical to scale undersea exploration and represents a significant advance toward more intelligent approaches to understanding the ocean and its inhabitants