CIOReviewIndia Team | Thursday, 02 January 2020, 12:17 IST
Today, with cognitive technologies taking the centre stage across many business verticals, deployment of AI and ML is increasing gradually. According a report by Algorithmia on State of Enterprise Machine Learning-2020 that includes inputs from 750 people from various companies engaged in building machine learning life cycles, companies are increasing their machine learning investments, but challenges related to model deployment, scaling and testing persist. Machine learning development still remains in the early stages in most enterprises, as per the report. “Enterprises across all industries are increasing their investments in machine learning, but significant room for growth and improvement remains. The model deployment lifecycle needs to continue to be more efficient and seamless for ML teams. Still, companies with established Machine Learning deployment lifecycles are benefiting from measurable results, including cost reductions, fraud detection, and customer satisfaction. We expect these trends to continue as ML technologies and processes arrive to market and are adopted,” said Diego Oppenheimer, CEO at Algorithmia.
Enhancing Customer Loyalty and Experience with Machine Learning
In its survey, Algorithmia provided a wide-ranging list of possible ML use cases along with a write-in option, and respondents were asked to select all answers that applied to their companies. The top five cases of companies centred on increasing customer loyalty, followed by generating customer insights and intelligence, and improving the customer experience. Fraud detection and reduction of overall costs are also considered to be the top use cases. Machine Learning is largely being used to run prediction modelling to make assessments about customer behaviour, while natural language processing is being used to analyze positive sentiment on the social media platforms. Cost reduction has also emerged as a potential business use case of ML in recent times.
The Road Ahead
Despite the rapid development in use cases and growth in AI and ML budget allocations, there is still a long road ahead to model deployment. 22 percent of respondents of the survey said that their companies have been in production stage with machine learning for a year while 50 percent said they spend between 8 and 90 days deploying a single machine learning model. 33 percent of the respondents cited scalability as the primary concern in their machine learning life cycle.
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