More and more companies are looking to leverage the power of artificial intelligence (AI) and machine learning (ML) to improve their customer experience, streamline production and boost agility.
However, the decision-makers tasked with making the AI dream a reality are worried that current infrastructure won’t meet the necessary requirements, a new study from Redis Labs has shown.
IT leaders understand that future models will heavily rely on real-time data, and 40 percent fear their infrastructure won’t suffice.
Already, most decision-makers (64 percent) say their firms are developing between 20 and 39 percent of their models on real-time data coming in from connected devices and various data streams. As this number grows, infrastructure will fall under increased strain.
Almost all (88 percent) of AI/ML decision-makers expect the number of use cases that require better infrastructure to increase in the next 24 months.
At the moment, infrastructure mostly struggles with reliability and performance, while some respondents also cited failure to meet security and compliance requirements.
For most, these problems could be solved by locating models in an in-memory database, as that would allow the businesses to prepare data more efficiently, improve analytics efficiency and keep data safer.
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