Half (48%) of developers believe machine learning projects are too time-consuming, according to new research from Civo.
The study of more than 500 developers revealed that one-quarter (24%) spend 11-20 hours each month configuring ML. The same number of developers also abandon between 26-50% of projects.
Civo says that, for many companies that are rapidly deploying ML, the amount of behind-the-scenes work can be frustrating for those in charge.
Machine learning takes too much worker time
The report explains that developers must first configure complex infrastructure, including machine resource management, monitoring, and feature extraction, before they can begin to generate ML insights.
However, the study’s purpose is not to discourage companies from exploring machine learning. Rather, it suggests a different route. Three-quarters (73%) said that open source helped to reduce the time they spent, from implementation to insight.
The majority (71%) saved up to 20 hours with thanks to open source, while more than one in 10 saved over 30 hours.
Only 11% of developers said that they had never abandoned a machine learning project, highlighting the high abandonment rate that is heavily influenced by time restraints.
Moreover, Civo Chief Innovation Officer Josh Mesout added: “As machine learning is becoming more common place as a problem solving tool, we have noticed many developers who are being tasked with deploying ML are not ML experts.”
Mesout added: “More needs to be done to highlight the benefits of open source tooling, which can significantly cut down on wasted time… With access to open source, developers can tap into the ready-made resources created by ML experts and spend their time generating the insights they need rather than configuring the infrastructure to get there.”
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