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The tech landscape is changing, the business applications are (potentially) game-changing, and, spoiler alert, Fergal very much believes the hype. Building ML products requires balance – it’s pointless to start with the problem if the solution is unattainable, but you shouldn’t start with the tech if it can’t meet real customer needs.
Machine Learning (ML) In the last few years, ML is proving to be a game changer for call centers and customer-facing organizations. This technology is crucial for analyzing customer sentiments and extracting insights from unstructureddata, such as social media comments or open-ended survey responses.
Deep learning algorithms are highly effective at processing complex and unstructureddata, such as images, audio, and text, and have enabled significant advances in a wide range of applications such as natural language processing, speech recognition, and image recognition systems that include facial recognition, self-driving cars, etc.
Betting trends, player statistics, outcome probabilities, and game retention are just some of the data points that most operators look at. AI systems can process both structured and unstructureddata at scale. Essentially, AI tools have overhauled the playbook on data science.
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