Almost half of developers think ML projects take up too much time

Before developers can generate machine learning insights, they need to configure different aspects of complex infrastructure, such as machine resource management, monitoring, and feature extraction, but many think this is too time consuming.

New research from Civo finds that of over 500 developers surveyed, 48 percent believe that ML projects take up too much time.

Of those surveyed 34 percent personally spend between zero and 10 hours configuring or setting up ML each month, with a further 24 percent spending 11 to 20 hours.

But with open source tools, developers can be far more flexible, deploy ML rapidly and gain insights. 73.2 percent say that open source reduces the time from implementation to insight, with 12 percent saving over 30 hours.

Josh Mesout, chief innovation officer of Civo, says:

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. Instead, they're domain experts in need of the insights and support of ML projects and tooling. However, the research shows that many are struggling to use this technology to its full potential, getting stuck in the surrounding infrastructure rather than reaping ML’s rewards.

More needs to be done to highlight the benefits of open source tooling, which can significantly cut down on wasted time. There are a range of emerging services available that can help to offset the widely found pain points of ML. 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.

The study also finds time pressures associated with ML deployment can be a significant factor in projects failing. 53 percent of ML developers abandon between one and 25 percent of projects, with an additional 24 percent leaving between 26 and 50 percent of projects and with percent between 51 and75 percent. Just 11 percent of developers say they have never abandoned an ML project.

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