Algorithmia has talked with thousands of people in various stages of machine learning (ML) maturity and in various roles connected to ML. Following the report we published last year , we conducted a two-prong survey this year, polling nearly 750 people across all industries from companies actively engaged in building ML lifecycles to those just beginning their ML journeys, finding that more than two-thirds of those who responded said their AI budgets are growing, while only 2 percent are cutting back.

• 40 percent of companies surveyed employed more than 10 data scientists, double the rate in 2018, when Algorithmia conducted its previous study. 3 percent employed more than 1,000 data scientists.
• Many respondents said they’re in the early stages, such as evaluating use cases and developing models.
• Many struggle with deployment. Half of those surveyed took between 8 days and three months to deploy a model. 5 percent took a year or more. Generally, larger companies took longer to deploy models, but the authors suggest that more mature machine learning teams were able to move faster.
• Scaling models is the biggest impediment, cited by 43 percent of respondents.

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