A lack of alignment between ML projects and the business can hobble efforts to scale the technology, said Will Kelly (@willkelly), a technical writer. “Data scientists and cloud engineers can overcome such challenges by taking a ‘land and expand’ approach,” Kelly said. “Such an approach leverages DevOps and cloud analytics, starting with small iterative pilot projects focused on business unit problems, then working up to enterprise-level production projects.”

Source: Overcoming the challenges of machine learning at scale | CIO

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