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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.
About Vertex AI Vision
Google’s Vertex AI Vision lets organizations seamlessly integrate computer vision AI into applications without the plumbing and heavy lifting. It’s an integrated environment that combines video sources, machine learning models, and data warehouses to deliver insights and rich analytics. Customers can either use pre-trained models available within the environment or bring custom models trained in the Vertex AI platform.
A Vertex AI Vision application starts with a blank canvas, which is used to build an AI vision inference pipeline by dragging and dropping components from a visual palette.
The palette contains various connectors that include the camera/video streams, a collection of pre-trained models, specialized models targeting specific industry verticals, custom models built using AutoML or Vertex AI, and data stores in the form of BigQuery and AI Vision Warehouse.
According to Google Cloud, Vertex AI Vision has the following services:Vertex AI Vision Streams: An endpoint service for ingesting video streams and images across a geographically distributed network. Connect any camera or device from anywhere and let Google handle scaling and ingestion.
Vertex AI Vision Applications: Developers can build extensive, auto-scaled media processing and analytics pipelines using this serverless orchestration platform.
Vertex AI Vision Models: Prebuilt vision models for common analytics tasks, including occupancy counting, PPE detection, face blurring, and retail product recognition. Furthermore, users can build and deploy their own models trained within Vertex AI platform.
Vertex AI Vision Warehouse: An integrated serverless rich-media storage system that combines Google search and managed video storage. Petabytes of video data can be ingested, stored, and searched within the warehouse.
According to Google Cloud, Vertex AI Vision has the following services:Vertex AI Vision Streams: An endpoint service for ingesting video streams and images across a geographically distributed network. Connect any camera or device from anywhere and let Google handle scaling and ingestion.
Vertex AI Vision Applications: Developers can build extensive, auto-scaled media processing and analytics pipelines using this serverless orchestration platform.
Vertex AI Vision Models: Prebuilt vision models for common analytics tasks, including occupancy counting, PPE detection, face blurring, and retail product recognition. Furthermore, users can build and deploy their own models trained within Vertex AI platform.
Vertex AI Vision Warehouse: An integrated serverless rich-media storage system that combines Google search and managed video storage. Petabytes of video data can be ingested, stored, and searched within the warehouse.
Website: International Research Awards on Computer Vision
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