Machine learning initiatives offer promising strategic value for enterprises. In fact, they expect AI and ML to improve all aspects of their businesses, and potentially to be disruptive in their industry sectors. And yet, these initiatives are still really challenging, requiring skills and resources many enterprises have yet to acquire. In fact, in our recent survey of nearly 300 data scientists, 7 in 10 respondents indicated they rely on external services for their AI or ML projects. Roughly 3 in 10 reported that they'd outsourced their data labeling.
Why outsource?
80% of respondents indicated that training AI/ML algorithms is more challenging than they expected, and nearly as many reported problems with projects stalling. AI/ML talent is rare and expensive, while data labeling is complex and time consuming. And note that among the many complex problems companies will benefit from solving themselves, data labeling is not one of them.
Our survey suggests that there's actually good reason for enterprises to rely on outside expertise to prepare their training data. The data shows that organizations that outsource their data labeling are significantly more likely to get their ML project into production.
For a deeper dive into these findings, check out our white paper What data scientists tell us about AI model training today. This white paper analyzes the findings and clearly defines what data science teams are looking for to advance their projects.
