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Learn which phases of the AI development lifecycle benefit the most from TCD optimization.
Note: the article assumes a fairly advanced understanding of TCD terms & concepts.
Artificial Intelligence is transforming the technology landscape of the digital age. The world is moving towards the adoption of AI-powered smart systems which will increase exponentially over the next few years. While we see the advancements, the key challenge would be the testing of Artificial Intelligence/Machine Learning (AI/ML)-based systems.
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The rest of the article covers phases 2-4 in more detail. Regarding phase 1, significant customization of the algorithm code is not as prominent and, to borrow the quote from Ron Schmelzer, “There’s just one way to do the math!”, so the core value proposition of Test Case Designer to explore possible combinations is not as relevant (i.e., low applicability due to the “linear” nature of operations).
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Phase 2
The general idea is to include each hyperparameter in the TCD model, breaking down the value lists based on the thresholds derived from theory or practical experience.
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Weakness: May explore the profiles with too many changes at a time or require numerous constraints to limit the scope.
Phase 3
Robo-advisors are a popular application of AI/ML systems in finance. They use online questionnaires that obtain information about the clients’ degree of risk-aversion, financial status, and desired return on investment. For this example, we will use Fidelity GO.
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3. The approach depends on the overall ability to leverage synthetic data instead of production copies which may or may not be feasible in your environment.
Phase 4
This phase is the closest to TCD’s “bread and butter”. The model would serve a dual purpose – 1) smoke testing of the AI; 2) integration testing of how it is operationalized.
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Weakness: Similar to Phase 3 but usually more manageable given the difference in goals (volume in P3 vs integration in P4).
Conclusion
To summarize, the applicability level by phase is repeated below:
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