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  • Journey to Data & AI Workshop
  • Prerequisite
    • IBM Cloud
  • IBM Watson Studio Auto AI
    • Watson Studio Overview
    • Step 1: Watson Service Creation
    • Step 2: Build and train the model
    • Step 3: Deploy the trained model
    • Step 4: Test the deployed model
  • IBM Watson Discovery
    • Discovery Overview
    • Step 1: Create Discovery Service
    • Step 2: Launch the tooling
    • Step 3: Create a collection
    • Step 4: Download the sample document and upload to your collection
    • Step 5: Querying the dataset
  • Links
    • Sample Application: Use the Watson Discovery Service to analyze cyber security breaches
    • Sample data set source
    • Preparing CSV data set to Watson Discovery Service
    • Watson Studio Documentation
    • IBM Cloud Documentation
    • Discovery API documentation
    • IBM Developer
    • Discovery documentation
    • Sample Codes: IBM Developer Code Patterns
    • Free courses: COGNITIVE CLASS.AI
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  1. IBM Watson Discovery

Discovery Overview

IBM Watson™ Discovery makes it possible to rapidly build cognitive, cloud-based exploration applications that unlock actionable insights hidden in unstructured data.

PreviousStep 4: Test the deployed modelNextStep 1: Create Discovery Service

Last updated 5 years ago

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This is the architecture of a complete Discovery solution:

Discovery architecture diagram

With Discovery, it only takes a few steps to prepare your unstructured data, create a query that will pinpoint the information you need, and then integrate those insights into your new application or existing solution.

How does Discovery do it? By using data analysis combined with cognitive intuition to take your unstructured data and enrich it so you can discover the information you need.

IBM Watson™ Discovery brings together a functionally rich set of integrated, automated Watson APIs to:

  • Crawl, convert, enrich and normalize data.

  • Securely explore your proprietary content as well as free and licensed public content.

  • Apply additional enrichments such as concepts, relations, and sentiment through Natural Language Understanding (NLU).

  • Simplify development while still providing direct access to APIs.