Preeti
Vision language models (VLMs) are a significant advancement in AI, combining large language models (LLMs) with computer vision to perform tasks like visual question answering (VQA). VLMs like Blip-2 and Llava can answer questions about images, with applications in diagram annotation, surveillance, and robotics. However, using VLMs in production poses unique challenges due to their non-standard nature, requiring more effort in tasks like dependency management and image data handling.
The C3 AI Platform enables seamless deployment and scaling of VLMs, supporting fault-tolerant deployments and providing tools for managing, monitoring, and scaling VLMs. The deployment process for VLMs is similar to that of LLMs, with an additional step of defining the VLM to ensure compatibility with required hardware and determine input and output profiles.
Key features of the C3 AI Platform for VLM deployment include security, highly optimized inference, versioning enforcement, and scalability. VLMs can be deployed securely within the user’s environment, run efficiently using accelerator libraries, enforced versioning through the Model Registry, and scaled from a single GPU to hundreds of GPUs.
Deploying VLMs on the C3 AI Platform unlocks new possibilities for enterprise AI applications, such as assisting in medical image diagnosis and enhancing surveillance systems. In the next installment, the series will cover managing LLMs, VLMs, and other large model deployments. Adam Gurary, a Senior Associate Product Manager at C3 AI, manages the platform’s model inference service and machine learning pipelines, focusing on building tools for hosting large language models and executing machine learning pipelines.
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Source link: https://c3.ai/blog/four-key-features-to-successfully-deploying-proprietary-and-open-source-vision-language-models/
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