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AI Model Robustness and Generalization Testing

“AI Model Robustness and Generalization Testing”

Advait A. Sowale

AI model robustness testing is crucial to ensure that machine learning models perform reliably across various scenarios and datasets. It helps identify potential vulnerabilities, biases, or inaccuracies in the model’s predictions, improving its overall reliability and trustworthiness. Generalization testing ensures that AI models can effectively generalize to new, unseen data beyond the training set, enhancing their real-world applicability. In this session we will go through different aspects associated with the AI Model Robustness and Generalization Testing


Meet Advait: A Pune-based IT Professional with a Decade of Diverse Expertise

Advait, an accomplished IT professional hailing from Pune, boasts an extensive career spanning over 10+ years, encompassing various domains such as Analysis, Designing, Development, Quality, and Delivery within the IT industry. Throughout his journey, he has contributed his skills and knowledge to renowned IT giants, catering to a global clientele.

Over the past five years, Advait has immersed himself in the realm of emerging technologies, honing his expertise in cutting-edge fields like Artificial Intelligence, Internet of Things, Mixed Reality, Blockchain, and Data Science, among others. His valuable insights and expertise have been sought after by international publications, where his blogs and articles have been published.

Adding to his repertoire, Advait is a seasoned speaker, having graced numerous events with captivating speeches and talks. He also imparts training on a wide array of technology-related topics, including Software Development Processes, Programming Languages, Software Technologies, Software Quality, Emerging Technologies, Digital Marketing, Content Creation, Content Management, Domain Knowledge, and much more.

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