June 9, 2023
ML technologies are fundamentally transforming the way businesses work. With the advancement of ML algorithms, computers can process data sets quickly and efficiently, enabling organizations to better process large amounts of information faster. In addition, the advancements in machine learning solutions and expertise have allowed for more accurate predictions of customer behavior and market trends. So it is only natural that the global ML spending is expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029, at a CAGR of 38.8%, according to Fortune Business Insights.
The growth in ML usage has resulted in various ways organizations can implement these technologies. From healthcare to retail to finance, machine learning unlocks new insights and improves operational performance. With so much potential and opportunities to leverage ML, let’s explore some of the most common examples of its use cases and applications across industries.
Scheme title: Worldwide AI software by category value, 2020-2021
Data source: idc.com — Artificial Intelligence Spending Grew 20.7% Worldwide in 2021, According to IDC
Computer vision is a subfield of ML that focuses on enabling computers to understand the content of digital images, videos, and other visual data. Computer vision use cases include facial recognition, object detection/classification, image segmentation, etc. and is relied upon across retail, healthcare, cybersecurity, and other sectors.
Natural Language Processing (NLP) is a subset of machine learning that allows computers to comprehend and interpret spoken and written human language. It is increasingly implemented in various sectors, including customer service, healthcare, and marketing automation, for tasks such as sentiment analysis, text classification, document summarization, and more.
Data mining uncovers patterns and discovers correlations, trends, and outliers in large data sets. Data mining use cases can be seen in various industries such as finance, healthcare, and retail for tasks like fraud detection, medical diagnostics, customer segmentation, and more.
Successfully implemented machine learning use cases can bring a range of benefits to organizations, including cost savings, improved efficiency, and better decision-making:
Businesses worldwide have already unlocked the benefits of machine learning and proved its infinite potential. From enhanced customer experiences and improved efficiency to better decision-making and reduced costs, the potential of ML is unparalleled compared to other technologies currently on the market. As such, it is wise for businesses to start taking advantage of this technology sooner rather than later to gain a competitive edge. However, companies must remember that the success of an ML project heavily depends on the unique business case of each organization, its goals, and available data. By taking all these factors into account before launching an ML project, businesses can ensure they get the most out of their investment.
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