THE STRATEGIC APPLICATION OF CLOUD-BASED MACHINE LEARNING AND DATA SCIENCE SOLUTIONS FOR ENHANCING BUSINESS RESILIENCE AND EFFICIENCY DURING CRISES IN THE U.S

ABSTRACT: This paper focuses on how cloud-based machine learning (ML) and data science solutions improve US business adaptability and performance through crises. Assembling a large amount of gathered data and performing econometric analysis, the research investigates how these technologies prevent disruptions, help to improve decision-making and guarantee business operations under unfavorable conditions. As a result, with the use of prediction and automation that originates from ML, the flow of operations can be easily adjusted to various changes, ensuring stability and healthy development. The work expands on literature that is already stressed on the importance of the digital agenda in uncertain circumstances. For example, Brynjolfsson and McAfee (2014) point out that more data leads to better decision-making and that firms using data have adapted better in turbulent economic environments. The COVID-19 pandemic is another perfect example of how fast cloud-based ML tools are integrated into the processes to prove their effectiveness. For instance, some logistics firms such as FedEx leveraged in machine learning to forecast interruptions of the supply chain and the best routes for delivery hence continuing to deliver despite the Covid-19 interventions reducing the volatility of demand patterns (McKinsey & Company, 2021). Real-world examples illustrate the practical applications and benefits of cloud-based ML and data science during crises. In the retail sector,