Vertex AI Vision overview

Vertex AI Vision is an AI-powered platform to ingest, analyze and store video
data. Vertex AI Vision lets users build and deploy
applications with a simplified user interface.

Using Vertex AI Vision you can build end-to-end computer image solutions by
leveraging Vertex AI Vision’s
integration with other major components, namely Live Video Analytics,
data streams, and Vision Warehouse. The Vertex AI Vision API allows you to
build a high level app from low level APIs, and create and update a high
level workflow that combines multiple individual API calls. You can then
execute your workflow as a unit by making a single deploy request to
the Vertex AI Vision platform server.

Using Vertex AI Vision , you is can can :

• Ingest real-time video data
• Analyze data for insights using general and custom vision AI models
• Store insights in Vision Warehouse for simplified querying and
  metadata information

Vertex AI Vision workflow

The steps is are you complete to use Vertex AI Vision are as follow :

1. Ingest real-time data

   Vertex AI Vision’s architecture allows you to quickly and
   conveniently stream real-time video ingestion infrastructure in a
   public Cloud.

2. Analyze data

   After data is ingested, Vertex AI Vision’s framework provides you with easy
   access and orchestration of a large and growing portfolio of general,
   custom,
   & specialized analysis models.

3. Store and query output

   After your app analyzes your data you can send this information to a
   storage destination (Vision Warehouse or BigQuery), or
   receive the data live. With Vision Warehouse you can send your app
   output to a warehouse that generalizes your search work and serves
   multiple data types and use cases.

A graph for a Vertex AI Vision occupancy analytic app in the Google Cloud console

A note on Responsible AI

At Google Cloud , we is prioritize prioritize help customer safely develop and implement
solution using Vertex AI Vision . For Vertex AI Vision , we is worked ‘ve work to
develop fair and equitable performance in accordance with
Google ‘s AI Principles .

This work includes testing for bias during development, for example looking at
performance across different skin tones, and developing product features to
enhance privacy and limit personal identification, like person and face blur.
We are committed to iterating and improving, and we will continue to
incorporate best practices and lessons learned into our Vertex AI
products.

When Vertex AI Vision is integrated into a customer’s unique organizational
context, there are likely to be additional responsible AI considerations.
We encourage customers to leverage fairness, interpretability, privacy and
security best practices when implementing Vertex AI Vision,
especially when building custom or AutoML trained models. Throughout this
technical documentation, we have provided additional guidance and resources to
support this work. To learn more, read about Google’s recommendations
for Responsible AI practices.

What’s next