Introduction: Generally, in the MLOps (methodology to develop ML based applications) we have design, develop and operations phases, wait something important is missing…I hope by now you got it, yes there is no testing phase in MLOps (like security, bias, performance etc.), but here is the question; how does ML applications are tested in order …
in-depth testing of AI applications that use images Read More »
In recent past there has been a spate of accidents involving AI and Machine learning models in practice and deployment. Much so that there is an active database of all such accidents being chronicled (https://incidentdatabase.ai/ ). At a time when AI is making strides in radical business transformation for enterprises, it is vital that we ensure …
10 Most Recommended Tests for your AI/ML/DL Models in 2022 Read More »
The current notions of quality assurance and testing in AI/ML pipelines is based on the idea of validation using a random set-aside set of data on which the model is tested and metrics computed thereof. Metrics like accuracy on the random set-aside data set termed ambiguously as test data, is the usual rubric for evaluation …
Why current testing processes in AI/ML are not enough? Read More »
Today’s Agile/DevOps setups need the ability to go faster. Availability of huge amount of diversified test data might be critical to success of Test Automation effort. In this article we will discuss about synthetic test data, it’s importance and applications, various options available to us today for generating cheap and adequate test datasets using modern Test …
Test data management using AI-powered synthetic data generators Read More »
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