Introduction
The ability to analyze massive data sets is achieved through statistical methods called data analytics. Data is collected from diverse sources and prepared for analysis to recognize patterns and trends for actionable insights. Artificial intelligence (AI) is machine processes that mimic human intelligence (Pries & Dunnigan, 2016). AI performs cognitive operations, including learning, social intelligence, and reasoning; thus, it can transform different fields, such as transport with driverless car introduction and surgery using robotics and more. AI can automate data analytics by providing the capability to quickly and accurately process large data sets. IBM’s Watson Studio and Microsoft Azure have popularly used AI technologies in data analytics, showing impressive capabilities.
Microsoft Azure and Watson Studio AI analytics tools
Watson Studio is AI software that performs analytics to identify patterns that inform decision-making. It integrates frameworks like TensorFlow and PyTorch that support data analytics, while Microsoft Azure has specialized capabilities such as language, speech recognition, and vision (Routled Chen et al., 2012). Firms that want to adopt AI and machine learning technologies have many tools to choose from to perform advanced analytics. The AI tools in the market are provided by technology companies such as Google, IBM, Microsoft, and Amazon, with the capability to transform the business intelligence landscape. These tools promote interoperability and stability with Watson Studio’s TensorFlow capability, running AI models that support analytics and decision-making (Shehu et al., 2022). On the other hand, Microsoft Azure has functions such as vision, speech, and language processing. These tools can support businesses in conducting advanced analytics for informed decisions.
Watson Studio and Microsoft Azure capabilities
Watson Studio is data analytics software provided by IBM Company, which enables analysts and data scientists to develop and run AI models for making optimal decisions. It has a powerful ML capability widely utilized in health and financial services (Tebepah, 2020). It offers deep learning capabilities needed to include artificial intelligence in enterprise operations to foster innovation. It allows for seamless use of chatbot for search services and customer engagement. It is an appropriate tool for less experienced.
Microsoft Azure has sophisticated capabilities for data normalization, preparation, and transformation, unlike Watson Studio. Moreover, unlike Watson Studio, it boasts built-in algorithms that include artificial neural networks and decision tree algorithms that facilitate quicker model training. (Sharda, et al., 2017). Azure integrates better with open-source technologies and provides opportunities for hosting services and applications. Azure utilizes natural languages for cognitive services that use machine learning models. This crucial feature allows for making sense of text data, speech, images, and videos to derive insights and value (Klochko et al., 2022). Azure is mature and utilizes machine learning capabilities that support web services such as Bing and Xbox. Microsoft Azure provides a better platform for creating high-performing models due to built-in algorithms. These tools are similar in creating advanced capabilities that employ tools like Python.
Organizations must first identify the business need and capacity for adopting these tools. The best tool is selected based on business requirements, compatibility and capability, and firm infrastructure (Bottou, 2013). The technologies are then interconnected with data sources, facilitating model training for data analytics and actionable insights.
Conclusion
AI automates data analytics by providing the capability to quickly and accurately process large data sets. IBM’s Watson Studio and Microsoft Azure have popularly used AI technologies in data analytics, showing impressive capabilities. Microsoft Azure provides a better platform for creating high-performing models due to built-in algorithms.
References
Bottou, L. (2013). From machine learning to machine reasoning. Machine Learning, 94(2), 133-149. https://doi.org/10.1007/s10994-013-5335-x
Klochko, O., Gurevych, R., Nagayev, V., Yu Dudorova, L., & Zuziak, T. (2022). Data mining of the healthcare system based on the machine learning model developed in the Microsoft Azure machine learning studio. Journal Of Physics: Conference Series, 2288(1), 012006. https://doi.org/10.1088/1742-6596/2288/1/012006
Pries, K. H., & Dunnigan, R. (2016). Big data analytics: A practical guide for managers (1st ed.). Auerbach Publications Print ISBN: 9781482234510
Routled Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
Sharda, R., Delen, D., & Turban, E. (2017). Business intelligence, analytics, and data science: A managerial perspective (4th ed.)
Shehu, J., Greca, S., & Xhina, E. (2022). Using machine learning algorithms in Microsoft Azure ML to improve system search. ISJM Volume 6, No.1 (2022), 6(1). https://doi.org/10.33807/monte.20222486
Tebepah, I. (2020). Digital Signal Processing for Predicting Stock Prices Using IBM Cloud Watson Studio. International Journal Of Computer Science And Engineering, 7(01), 7-11. https://doi.org/10.14445/23488387/ijcse-v7i1p102
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