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Introduction

Cloud Vision plugins will allow users to use pre-trained Vision API models to detect emotion, understand text, and more. They will be useful in enriching data with additional attributes such as labels, faces, etc.

NOTE: These plugins will incur additional cost of the Cloud Vision APIs

Use case(s)

  • As a user, I want to various features in my images and documents using the Cloud Vision API, so that I can add ML-driven enrichments to my Data Fusion pipelines that process unstructured data
  • As a user, I want easy, UI-driven ways of manipulating and understanding the output of the Cloud Vision API, so that I do not need to write any code for parsing it.

User Storie(s)

Plugin Type

  • Batch Source
  • Batch Sink 
  • Real-time Source
  • Real-time Sink
  • Transform
  • Action
  • Post-Run Action
  • Aggregate
  • Join
  • Spark Model
  • Spark Compute

Configurables 

File Path Batch Source

SectionUser Facing NameTypeDescriptionConstraints
BasicAccess Token



FeaturescheckboxesThe features to extract from documents. Select from




















Image Extractor transform

SectionUser Facing NameTypeDescriptionConstraints
BasicAccess Token



FeaturescheckboxesThe features to extract from documents. Select from




















File Extractor transform

SectionUser Facing NameTypeDescriptionConstraints
BasicAccess Token



FeaturescheckboxesThe features to extract from documents

























Design / Implementation Tips

  • Tip #1
  • Tip #2

Design

Approach(s)

Properties

Security

Limitation(s)

Future Work

  • Some future work – HYDRATOR-99999
  • Another future work – HYDRATOR-99999

Test Case(s)

  • Test case #1
  • Test case #2

Sample Pipeline

Please attach one or more sample pipeline(s) and associated data. 

Pipeline #1

Pipeline #2



Table of Contents

Checklist

  • User stories documented 
  • User stories reviewed 
  • Design documented 
  • Design reviewed 
  • Feature merged 
  • Examples and guides 
  • Integration tests 
  • Documentation for feature 
  • Short video demonstrating the feature
  • No labels