How to Deploy Computer Vision Models: Best Practices
Blog post from Roboflow
The guide provides a comprehensive overview of various deployment methods for computer vision models, focusing on cloud and edge deployment strategies. It highlights the importance of selecting an appropriate deployment method based on specific business needs and factors such as real-time processing requirements, internet connectivity, and data types being processed. Cloud deployment offers scalability and ease of management, but may introduce latency, while edge deployment reduces latency and enhances data privacy, although it may complicate management. The guide emphasizes that deployment decisions should align with the application's business logic rather than solely technical considerations, and it discusses Roboflow Inference as a tool to facilitate edge deployments. For applications requiring real-time action, edge deployment is recommended, whereas cloud deployment suits scenarios with stable internet access and non-real-time processing needs.