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Business leaders understand the advantage of using the power of artificial intelligence and machine learning to stay ahead of their competitors. However, understanding the power of AI is a lot different than actually successfully implementing it in companies. For example, in 2017, Gartner estimated that Big Data projects have a success rate of only 15%. While organizational factors may be a primary reason for this poor success rate, another reason for such a high failure rate could be due to a lack of AI / Machine Learning talent needed to successfully pursue these types of projects. Specifically, it’s been shown that there is a lack of advanced machine learning talent among data professionals; less than 20% of surveyed data professionals said they were competent in such areas as Natural Language Processing (19%), Recommendation Engines (14%), Reinforcement Learning (6%), Adversarial Learning (4%) and Neural Networks – RNNs (15%).

So, how do companies leverage the power of AI with these skills gaps? IBM is using the power of its Watson Studio platform to extend the power of AI to many more people who fall outside of AI expert coders. IBM Watson Studio is an end-to-end analytics and AI solution to help you gain insights from your data. Watson Studio accomplished this feat by providing the option to build custom models or leverage pre-trained models.
In this article, I will briefly review several capabilities of Watson Studio and compare two machine learning models that predict customer churn of mobile users. Read full article here.