Integration of Cloud Computing, Artificial Intelligence, and Machine Learning for Enhanced Data Analytics
Keywords:
cloud computing, artificial intelligence, machine learning, data analytics, data-driven decision making, predictive modeling, anomaly detection, ethical considerationsAbstract
With more and more organisations looking to get important insights from their massive data sets, data analytics has grown in importance in today's data-driven world. One effective strategy for improving data analytics is the combination of cloud computing, AI, and ML. This research paper explores the synergistic relationship between these technologies and their collective impact on data analytics. The paper begins by providing an overview of cloud computing, AI, and ML, highlighting their individual strengths and how they can be leveraged in the context of data analytics. It then delves into the integration of these technologies, discussing the benefits, challenges, and best practices for effective implementation. The study examines several use cases and real-world applications where the integration of cloud computing, AI, and ML has led to improved data analytics, such as predictive modeling, anomaly detection, and decision support. The paper also presents a comparative analysis of different cloud-based AI and ML platforms, evaluating their features, performance, and suitability for various data analytics scenarios. Furthermore, the research explores the ethical considerations and regulatory implications surrounding the use of these integrated technologies, addressing issues like data privacy, algorithmic bias, and transparency.The article finishes by suggesting next steps for businesses interested in using cloud computing, AI, and ML for improved data analytics, as well as by describing current trends and possible developments in this space.
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