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SUMMARY:Limits and Leverage of AI in Test Data
DESCRIPTION:Limits and Leverage of AI in Test Data (register here) \nHear what Joe Colantonio\, host of the Test Guild\, has to say about the webinar (watch the trailer) \nArtificial Intelligence continues to transform testing landscapes\, often promising to drastically reduce complexity and make automation a breeze. Yet\, when it comes to reliable test data\, AI alone cannot replace the nuanced reality of production data – especially for reproducing elusive errors and rare event constellations. Plus\, the organizational challenge of different data consumers (developers\, testers\, automation engineers\, ML engineers) wanting different things is often overlooked. In this context expert knowledge and field experience are key – both on the operator as well as the consumer side. AI and automation will certainly lighten the load\, but they must be applied wisely within a holistic framework. \nIn this insightful webinar in partnership with TestGuild\, Christoph Stock and Bastian Baudisch from UBS Hainer will examine the complexities of supplying test data across diverse environments\, tailored to the individual consumers’ needs. They will share how to separate technical from organizational complexity and how artificial intelligence CAN help when employed in the right context. \nKey Takeaways Include: \n\nThe Intricacies of Test Data Orchestration: Understand the multifaceted challenges of providing test data to numerous consumers while maintaining quality\, security\, reliability and relevance.\nProduction Data vs. AI-Generated Data: Learn where AI generated data has its place and where real-world production data is indispensable.\nAugmentation\, Not Replacement: Discover how to strategically leverage AI to enhance your testing arsenal\, using intelligent data augmentation to fill gaps or increase volume.\nSmart Integration for Future-Proofing: Explore a balanced approach that merges the best of AI-driven innovation with the proven reliability of real production data\, ensuring compliance and performance.\n\nJoin us for a thought-provoking session that cuts through the hype and delves into practical realities. Whether you’re a tester\, QA professional\, ML engineer or technology leader\, this discussion will empower you to make informed choices and shift from a reactive process to a proactive data strategy. \nEmbrace a sophisticated\, hybrid model where AI augments the irreplaceable insights provided by production data and automation boosts your productivity\, improving lead times as well as the developer experience. \nSpeakers \nBastian Baudisch \n\n\n\nBastian is a data management and process automation specialist with a multifaceted background in technical pre-sales and consulting in finance\, insurance\, retail and pharma. In his current role as Lead Sales Engineer US at UBS HAINER\, he drives the adoption of the XDM Data Orchestration Platform — enabling enterprise software engineers\, testers\, and machine learning teams to access production-grade\, real-world data on demand. With previous leadership roles in data science and software QA\, Bastian has honed a pragmatic\, holistic approach to solving complex data challenges. Bastian’s passion lies in bridging technological innovation with practical\, user-centric solutions\, ensuring that test environments are as robust and agile as the challenges they face. \n  \n\n\n\nChristoph Stock \nChristoph Stock is an experienced software expert with over 15 years of experience in automating test data management. With a background in computer science\, Christoph has extensively collaborated with companies in the finance\, insurance\, and retail industries\, supporting them in the implementation and migration of test data management solutions. His passion lies in developing software and automation processes that are tailored to user needs
URL:https://ubs-hainer.com/de/event/limits-and-leverage-of-ai-in-test-data/
LOCATION:Online
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/png:https://ubs-hainer.com/wp-content/uploads/2025/05/Screenshot-2025-05-20-at-11.46.29 AM.png
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