Can’t hire fast enough? How to deliver value from your AI with a small data team
Enterprise data science teams are having trouble hiring data scientists and machine learning engineers fast enough. But there is no need to wait until your team is fully staffed to start generating value from your AI. It’s just a question of bringing together automation and better tooling so that even small data science teams can have an outsized impact.
In engaging with chief data and analytics officers, one of the most common themes I hear is the difficulty in hiring and retaining data scientists and machine learning engineers. Major data science initiatives will come to a halt when a principal data scientist leaves or efforts to integrate AI throughout the enterprise become just a trickle of projects due to the inability to hire ML engineers quickly enough to manage each model in production.
And this is a widespread issue. According to a 2021 Gartner survey, nearly two thirds (64%) of IT executives identified lack of skilled talent as the biggest barrier to adopting emerging technologies like AI and machine learning. Hiring for data scientists takes 20% longer than IT jobs as a whole, and more than twice as long as the average US corporate job. Demand for ML engineers is even more intense, with job openings for ML engineers growing 30x faster than IT services as a whole.
Enterprises have poured billions of dollars into AI (investments that have included the expansion of data teams) based on promises around increased automation, personalizing the customer experience at scale or delivering more accurate predictions to drive revenue. But so far there has been a massive gap between AI’s potential and the outcomes, with only about 10% of AI investments yielding significant ROI.
For CDAOs, this is a key question. How can they deliver value from applied AI/ML throughout the enterprise in the near term with only a small number of data scientists and possibly even fewer ML engineers? Put another way, can small data science teams start driving outsized value without waiting months or years for a fully staffed, fully trained team?
Instead of waiting until they fill these roles, MLOps teams need to find a way to support more ML models and use cases without a linear increase in data science headcount. So how do they do that? Some tips include:
Different team members bring different strengths and skills to the team. Data Scientists excel at turning data into models that help solve business problems and make business decisions. But the expertise and skills required to build great models aren’t the same skills needed to push those models in the real world with production-ready code, and then monitor and update on an ongoing basis. On the other side, ML Engineers integrate tools and frameworks together to ensure the data, data pipelines, and key infrastructure are working cohesively to productionize ML models at scale.
But while data scientists may be happy to hand their models over to the MLOps team for a production rollout, this process may not be efficient. Because data scientists and MLOps engineers don’t speak the same language, and don’t work or think the same way, there can often be time-consuming bottlenecks as one group tries to articulate a requirement (e.g. data preprocessing required), and the other team tries to satisfy it.
