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Brianna White

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Jul 30, 2019
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Artificial intelligence (AI) is gaining a lot of traction lately. Apparently, the majority of AI services and products will be in high demand for the next few years. According to Gartner, worldwide AI software revenue is forecast to total $62.5 billion in 2022, and one-third of organizations with AI technology plans said they would invest $1 million or more in the next two years. 
And when we talk about AI, there is always another topic to discuss —machine learning (ML) methods. 
The upheaval of 2020 forced companies to be laser-focused on their most important priorities — among them, of course, are AI and ML initiatives. According to an Algorithmia report, 83% of organizations have increased AI or ML budgets year-over-year. It's no surprise when you consider ML models can generalize and perform complex tasks.
But businesses are struggling when it comes to building AI solutions that can quickly scale. When implementing ML models across different industries, they allow current businesses to scale even faster. ML helps to automate everything, including decision-making, pricing, customer support and more tasks.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/01/25/machine-learning-and-artificial-intelligence-implementation-in-practice/?sh=699adb855c89
 

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