Machine learning is enabling a smooth shift in this COVID-19 struck world.
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Machine learning is one of the most used technologies in this generation. It has varied capabilities that can transform businesses across industries for the better. From being considered as a niche technology, is now seeing an increased adoption within companies in all sectors.
From a global perspective, brands are leveraging to accelerate innovation and better customer experience. For example, Nike uses for personalized product recommendations. In the F&B industry, Dominos maintains its 10 minutes or less pizza delivery time using technologies. Another widely used example is how automobile giant BMW uses to analyze data from vehicle subsystems and predicts the performance of vehicle components and recommends when they should be serviced.
In 2020, became a priority for tech companies in order to achieve revenue growth while reducing costs. In 2021, those companies are now exploring many matured applications of this technology. Disruptive tech organizations have been leading this technology across many areas like process automation, customer experience, and security.
The coronavirus global pandemic has highlighted the importance of investing on and optimizing the healthcare systems. Machine learning is being considered as the most promising technology that enables healthcare providers to generate large volumes of data for insightful clinical decisions. Machine learning also enables huge processes in drug discovery, cutting down the long discovery and development time and reducing overall costs. It can also improve healthcare delivery systems to better the overall quality of healthcare under low costs. In the future, is predicted to be a critical part of clinical trials. Including pharmaceuticals and the biotech industry, will be having a huge impact in all aspects.
Banking and Finance Sector
The banking sector is already seeing many advanced use cases of , especially when it comes to fraud detection and automating processes. Machine learning applications will be proactively explored in areas in trading, investment modeling, risk prevention, and customer sentiment analysis. As countries are making digital transactions their primary mode of payment, is combining to play a pivotal role in helping financial companies to improve transaction efficiencies within the entire transaction lifecycle. Banks and financial institutions will also use technology to customize their banking products and offerings to stay up to date in the competitive environment.
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