In a recent study we conducted, we found the significant time challenges developers face in machine learning (ML) projects. The study, which surveyed over 500 developers, found that nearly half (48%) believe ML projects are excessively time-consuming.

The research highlighted that a substantial portion of developers' time is consumed by the configuration of ML projects. 24% of those surveyed spend between 11-20 hours each month on this task alone. This extensive time investment has led to a high project abandonment rate, with developers discarding 26-50% of their ML projects.

Machine learning projects demand intricate setup processes, including the management of machine resources, monitoring, and feature extraction. These preliminary steps are essential before developers can even start deriving ML insights.

Josh Mesout, Chief Innovation Officer at Civo, commented on the findings:

“As machine learning increasingly becomes a standard tool for problem-solving, we observe many developers tasked with deploying ML are not ML experts.”

He emphasized the need for greater awareness of the benefits of open-source tooling, which can significantly reduce wasted time. Mesout added,

“With access to open-source, developers can leverage the resources created by ML experts, focusing their efforts on generating insights rather than configuring the infrastructure.”

At Civo, we’re committed to addressing these challenges by providing developers with efficient, cloud-native solutions that streamline the ML project lifecycle. By harnessing the power of open-source tools and reducing the time spent on configuration, we aim to make ML more accessible and less daunting for developers.

For more insights from Josh Mesout, read the full article on TechRadar.