Comet, the provider of the leading development platform for enterprise machine learning (ML) teams, announced the results of its recent survey of machine learning professionals.
Hundreds of enterprise ML team leaders were asked about their experiences and the factors that affected their teams’ ability to deliver the level of business value their organizations expected from ML initiatives. Rather than attaining desired outcomes, however, many survey respondents revealed that they lack the right resources, or they shared that the resources they have are often misaligned. As a result, many AI initiatives have been far less productive than they could be.
According to survey respondents:
- Significant time, resources and budgets are being wasted.
- There is a serious lag in model deployment.
- Budgets for tools that could address issues are woefully inadequate.
- Without funds for automation, ML teams must track experiments manually.
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