AI Testing: How to Ensure the Reliability of Your Intelligent Systems
AI Testing: How to Ensure the Reliability of Your Intelligent Systems
AI
QA Engineering
Test automation
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Artificial intelligence offers enormous potential, but only when the systems function correctly and deliver the desired results. This is where AI testing comes into play. As your collaborative QA expert, we take a look at the critical testing stages. Because prevention begins with a systematic approach.
Artificial intelligence offers enormous potential, but only when the systems function correctly and deliver the desired results. This is where AI testing comes into play. As your collaborative QA expert, we take a look at the critical testing stages. Because prevention begins with a systematic approach.
The successful implementation of AI-based systems requires a fundamentally new quality assurance strategy. Only in this way can it be guaranteed that the models not only function but also make reliable, unbiased decisions. The classic testing phases form the foundation of prevention.
We start with unit and integration tests. Unit tests, also known as component tests, can be applied to all non-model components and are known from conventional systems. Integration tests verify, as the next step, the interaction of various components of the system, including the interaction between AI and non-AI components, to ensure that they work together correctly and deliver the expected results.
The successful implementation of AI-based systems requires a fundamentally new quality assurance strategy. Only in this way can it be guaranteed that the models not only function but also make reliable, unbiased decisions. The classic testing phases form the foundation of prevention.
We start with unit and integration tests. Unit tests, also known as component tests, can be applied to all non-model components and are known from conventional systems. Integration tests verify, as the next step, the interaction of various components of the system, including the interaction between AI and non-AI components, to ensure that they work together correctly and deliver the expected results.
System and Acceptance Tests
Furthermore, system tests ensure that the overall system functions as expected with all integrated components. The uniqueness of AI-based systems lies in the re-verification of the functional performance criteria of machine learning. This ensures that the test results of the original ML model tests are not affected when the model is integrated into a complete system.
The acceptance test ultimately serves customer acceptance. Here, the challenge is in a clear definition of the acceptance criteria that accurately reflect expectations for AI performance.
A holistic solution includes mastering these complex testing phases.
System and Acceptance Tests
Furthermore, system tests ensure that the overall system functions as expected with all integrated components. The uniqueness of AI-based systems lies in the re-verification of the functional performance criteria of machine learning. This ensures that the test results of the original ML model tests are not affected when the model is integrated into a complete system.
The acceptance test ultimately serves customer acceptance. Here, the challenge is in a clear definition of the acceptance criteria that accurately reflect expectations for AI performance.
A holistic solution includes mastering these complex testing phases.
AI-Specific Testing Procedures
Testing AI-based systems requires specific techniques and methods. In addition to the classical testing stages, AI-specific procedures are added: input data tests and ML model tests.
The input data test ensures the quality of the data used by the system and includes techniques such as reviews, static techniques, exploratory data analysis of the training data, as well as static and dynamic tests of the data pipeline. The ML model test validates selected models against the fulfillment of all established functional and non-functional performance criteria. If these criteria are not met, adjustments such as optimizing hyperparameters or changing the algorithm can be made.
Guaranteed quality for your AI solutions is not a coincidence. It requires a holistic prevention approach at every testing stage. Let us work together to ensure the reliability of your AI-based systems. Contact us for a non-binding conversation about your AI testing and quality strategy.
AI-Specific Testing Procedures
Testing AI-based systems requires specific techniques and methods. In addition to the classical testing stages, AI-specific procedures are added: input data tests and ML model tests.
The input data test ensures the quality of the data used by the system and includes techniques such as reviews, static techniques, exploratory data analysis of the training data, as well as static and dynamic tests of the data pipeline. The ML model test validates selected models against the fulfillment of all established functional and non-functional performance criteria. If these criteria are not met, adjustments such as optimizing hyperparameters or changing the algorithm can be made.
Guaranteed quality for your AI solutions is not a coincidence. It requires a holistic prevention approach at every testing stage. Let us work together to ensure the reliability of your AI-based systems. Contact us for a non-binding conversation about your AI testing and quality strategy.
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