Overcoming the challenges of machine learning at scale

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Machine learning (ML) and artificial intelligence (AI) technologies are increasingly on the investment list for IT leaders. Among the many benefits of these technologies, building and deploying ML models can add automation to mundane, repetitive tasks and let humans focus on more mission-critical work. ML also gives businesses the power to extract meaning from the massive amounts of data they are collecting and generating annually.The productivity and analytics gains are not lost on business leaders. In IDG’s 2019 Digital Business Study, 78% of IT and business leaders said their organization is considering or has already deployed machine learning technologies as part of their digital business strategy. And as CIO.com observes, machine learning is one of the highest in-demand skills in today’s technology job market.As ML models gain more traction throughout the enterprise, we asked our IDG Influencer community of experts about the biggest challenges to scaling machine learning across the enterprise, and the best ways to overcome those challenges. Here’s a summary of their insights.Time and complexityCommon roadblocks as organizations look to scale machine learning across the enterprise are, perhaps not surprisingly, time and complexity.“One challenge we regularly encounter when working with clients is …

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