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Pie chart with information on AI/ML budgets

More and more companies are developing machine learning models for internal use. But many are still struggling to bridge the gap to practical deployments.

What’s new: Many companies haven’t figured out how to realize their AI ambitions, according to a report by Algorithmia, a marketplace for algorithms. Although AI budgets are on the rise, only 22 percent of companies using machine learning have successfully deployed a model, the study found.

What the report says: The 2020 State of Enterprise Report is based on a survey of nearly 750 people including machine learning practitioners, managers overseeing machine learning projects, and executives at large tech corporations.

  • More than two-thirds of the subgroup that was asked about budgets reported increased spending on AI between 2018 and 2019 (see the graph above).
  • Nonetheless, 43 percent of respondents cited difficulty scaling machine learning projects to their company’s needs, up 13 percent from last year’s survey.
  • Half of respondents said their company takes between a week and three months to deploy a model. 18 percent said it takes from three months to a year.

Why it matters: AI is rapidly expanding into new applications and industries, and research is making tremendous strides. Yet building successful projects is still difficult. This report highlights both the great value of practical experience in the field and the need to establish effective practices and processes around designing, building, and deploying models.

We’re thinking: There’s a huge difference between building a Jupyter notebook model in the lab and deploying a production system that generates business value. AI as a field sometimes seems crowded but, in fact, it’s wide open to professionals who know what they’re doing.


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