Google’s Vertex AI Vision brings no-code to computer vision

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Vertex AI Vision combines video sources, machine learning models, and data warehouses to deliver rich insights and computer vision analytics without the heavy lifting.

Developing and deploying vision AI applications is complex and expensive. Organizations need data scientists and machine learning engineers to build training and inference pipelines based on unstructured data such as images and videos. With the acute shortage of skilled machine learning engineers, building and integrating intelligent vision AI applications has become expensive for enterprises.

On the other hand, companies such as Google, Intel, Meta, Microsoft, NVIDIA, and OpenAI are making pre-trained models available to customers. Pre-trained models like face detection, emotion detection, pose detection, and vehicle detection are openly available to developers to build intelligent vision-based applications. Many organizations have invested in CCTV, surveillance, and IP cameras for security. Though these cameras can be connected to existing pre-trained models, the plumbing needed to connect the dots is far too complex.

Building vision AI inference pipelines

Building a vision AI inference pipeline to derive insights from existing cameras and pre-trained models or custom models involves processing, encoding, and normalizing the video streams aligned with the target model. Once that’s in place, the inference outcome must be captured along with the metadata to deliver insights through visual dashboards and analytics.

For platform vendors, the vision AI inference pipeline presents an opportunity to build tools and development environments to connect the dots across the video sources, models, and analytics engine. If the development environment delivers a no-code/low-code approach, it further accelerates and simplifies the process.

Source – https://www.infoworld.com/article/3680148/googles-vertex-ai-vision-brings-no-code-to-computer-vision.html

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