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Enterprises have poured billions of dollars into artificial intelligence based on promises around increased automation, personalizing the customer experience at scale, or delivering more accurate predictions to drive revenue or optimize operating costs. As the expectations for these projects have grown, organizations have been hiring more and more data scientists to build ML models. But so far there has been a massive gap between AIs potential and the outcomes, with only about 10% of AI investments yielding significant ROI.

When I was part of the automated trading business for one of the top investment banks a decade ago, we saw that finding patterns in the data and building models (aka, algorithms) was the easier part vs. operationalizing the models. The hard part was quickly deploying the models against live market data, running them efficiently so the compute cost didnt outweigh the investment gains, and then measuring their performance so we could immediately pull the plug on any bad trading algorithms while continuously iterating and improving the best algorithms (generating P&L). This is what I call the last mile of machine learning.

Today, line of business leaders and chief data and analytics officers tell my team how they have reached the point that hiring more data scientists isnt producing business value. Yes, expert data scientists are needed to develop and improve machine learning algorithms. Yet, as we started asking questions to identify the blockers to extracting value from their AI, they quickly realized their bottleneck was actually at the last mile, after the initial model development.

As AI teams moved from development to production, data scientists were being asked to spend more and more time on infrastructure plumbing issues. In addition, they didn't have the tools to troubleshoot models that were in production or answer business questions about model performance, so they were also spending more and more time on ad hoc queries to gather and aggregate production data so they could at least do some basic analysis of the production models. The result was that models were taking days and weeks (or, for large, complex datasets, even months) to get into production, data science teams were flying blind in the production environment, and while the teams were growing they weren't doing the things they were really good at.

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.

ML engineers are responsible for integrating tools and frameworks together to ensure the data, data engineering pipelines, and key infrastructure are working cohesively to productionize ML models at scale. Adding these engineers to teams helps put the focus back on the model development and management for the data scientists and alleviates some of the pressures in AI teams. But even with the best ML engineers, enterprises face three major problems to scaling AI:

To really take their AI to the next level, todays enterprises need to focus on the people and tools that can productionize ML models at scale. This means shifting attention away from ever-expanding data science teams and taking a close look at where the true bottlenecks lie. Only then will they begin to see the business value they set out to achieve with their ML projects in the first place.

See the article here:

Why Hiring More Data Scientists Won't Unlock the ROI of Your AI - InformationWeek