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Testing AI applications : Elements of AI/Machine learning testing

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V2Soft

AI Application Testing

 

Quality Assurance is an important part of any innovation or product delivery that it's quite possibly the most fundamental segment of any product development. 

However, Testing AI applications is different than conventional programming testing. You can't simply send the AI or Machine learning models to the production, since AI frameworks need to test exceptionally appropriate way which ought to fulfill quality attributes like execution, strength, dependability, security, and ease of use. After testing every one of these boundaries, at that point it is good to deploy to the production

The most recent stats show artificial intelligence is one of the most growing technology in the IT business. 

What are the elements of AI/Machine learning testing?

  • Dataset
  • Training Data
  • Test Data
  • Model 
  • Training Phase
  • Inference Phase
  • Source Code
  • Input and Output Values
  1. To begin with, we need to ensure the AI algorithm itself works well or not.

  2. Inference Phase: This will be done according to real-world data. 

  3. Dataset: It infers the Basic information of Input. 

  4. To begin with, we need to ensure the AI algorithm itself works well or not.
  5. Test Data: this testing will be done to check if the model functions as excepted 
  6. Training Phase: In this stage, we need to test two things Taring data and the hyperparameter design information. By utilizing validation techniques, you ought to check that training data does a good job of classifying or regressing data with sufficient generalization without overfitting or underfitting the data.

  7. Input and Output Values: Input and output values are more important in any software product; you should know what is input data you need to validate and what is the expected outcome. 

 
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