Designing and Deploying Image Captioning Systems with BLIP-2

Deploying large vision-language models in production requires more than just loading a pre-trained model; it demands smart architecture and efficient resource management. This course guides you through the process of designing and launching a scalable image captioning system. You will transition from running basic model inferences to designing a production-ready deployment pipeline. You will learn how to estimate memory requirements, optimize model serving, and structure the supporting infrastructure to handle real-world traffic efficiently. What you'll learn: Understand the foundational architecture of vision-language models like BLIP-2; Estimate hardware and memory requirements for serving large models in production; Design scalable API endpoints to handle image uploads and caption generation; Apply model optimization techniques, including quantization, to reduce latency; Configure containerized environments for reliable deployment across cloud platforms; Implement basic monitoring and logging to track system performance and resource usage. The course begins with core image captioning concepts and model architectures, then progresses through resource estimation, API design, and container-based deployment strategies. Designed for software engineers, aspiring machine learning engineers, and developers looking to deploy AI models, this course requires no advanced machine learning background. Start reading today to master the engineering behind scalable vision-language deployments.