With the rapid development of network, network monitoring is a significance means to ensure network security and reliability. In-band network telemetry (INT) can collect items in line-rate, and support large traffic volumes and rates network telemetry. However, existing INT monitoring schemes are quite limited in flexibly expanding the execution monitoring tasks. In this paper, we propose Fast-INT, an efficient network monitoring framework combined with learning. The goal of Fast-INT is to design a light-weight INT network collection framework by quickly implementing dynamic and scalable collection of network status information. In our approach, an INT scheduling algorithm based on reinforcement learning is designed to dynamically deploy and adjust INT monitoring tasks when dealing with network inner change event. Particularly, Fast-INT can implement specific INT monitoring tasks on target point to shorten the time of monitoring and make the network monitoring more efficient. The evaluate results show that Fast-INT has a good performance on network monitoring and achieves the goal of intelligently deploying network monitoring tasks.