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The Role of Machine Learning in Modern Software Development
Explore how machine learning is used in modern software development. Learn about its impact on automation, problem-solving, and enhancing user experiences in today’s tech-driven world. AI and ML development tools, including TensorFlow, PyTorch, and Scikit-learn, are the features of modern software trends as they capture the way applications are designed and deployed.
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“Fortinet has spent the past decade maturing its AI and ML technology to analyze over 100 billion global security events a day covering
Fortinet announced enhanced AIOps capabilities across its entire networking portfolio, including the industry’s first #artificialintelligence based #networkork operations management for #5G #LTE gateways.
Innoviti Payment Solutions, announced a significant upswing in the volume contribution of the fast-growing UPI payment platform to its overall payment transaction processing in offline retail stores after introduction of ML-based Path Predictor technology. Path Predictor, a patented technology of Innoviti, uses Machine Learning to decipher patterns lying deep within millions of transactions passing through Innoviti to create models that dynamically and in real-time select the best path for a UPI transaction, leading to dramatic change in UPI success rates and a seamless consumer experience. Since introduction of this technology, over the recent 3 months of Q2 2019, UPI@POS transactions at Innoviti have grown to contribute nearly 2% of Innoviti’s overall transaction processing volume.This seamless experience driven by deep integration with Google Pay resulting in usage surge.
Innoviti Payment Solutions, announced a significant upswing in the volume contribution of the fast-growing UPI payment platform to its overall payment transaction processing in offline retail stores after introduction of ML-based Path Predictor technology. Path Predictor, a patented technology of Innoviti, uses Machine Learning to decipher patterns lying deep within millions of transactions passing through Innoviti to create models that dynamically and in real-time select the best path for a UPI transaction, leading to dramatic change in UPI success rates and a seamless consumer experience. Since introduction of this technology, over the recent 3 months of Q2 2019, UPI@POS transactions at Innoviti have grown to contribute nearly 2% of Innoviti’s overall transaction processing volume.This seamless experience driven by deep integration with Google Pay resulting in usage surge.
