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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.
“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.
