Why isn’t your AI delivering ROI?

How to bridge the production gap between data and IT

A rising graph line with a robot hand pointing to the highest point.

Data scientist has been one of the superstar IT roles of recent years, with the promise of spinning data into gold with the application of AI and machine learning.

Using cutting-edge technology to extract deep insights from reams of business data, data scientists aim to help guide their organisations into a more innovative, efficient and profitable future. But so far, return on investment hasn’t always been what companies might hope.

“One of the biggest mysteries in data science today actually has very little to do with data science: What is that last mile to AI ROI?” says Sivan Metzger, managing director MLOps and governance at DataRobot. “You build your machine learning, you find the data, you get it cleaned up, you build the models, you try 90 different iterations, you make a good and clean one and it’s ready to go. What happens then? Why are we not seeing value at scale from AI?”

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Metzger credits these issues to a disconnect between the data team, IT operations and stakeholders on the business side (i.e. the potential consumers of data science insights). Data science and IT operations teams have very different considerations and goals – and machine learning is very different from running software. This disconnect is known as the ‘production gap’, and can prevent AI solutions from being properly executed.

Machine Learning Operations (MLOps) is a combination of processes, best practices and underpinning technologies which seeks to bridge this gap by increasing collaboration and communication between data scientists and operations staff – and ultimately ensuring that AI is properly deployed and can begin to deliver the ROI promised.

To learn more about how MLOps can improve your returns on AI, watch IT Pro and DataRobot’s webinar ‘The Last Mile to AI ROI’, in which Metzger and data scientist Rajiv Shah discuss topics including:

  • How to eliminate AI-related risks by adopting MLOps best practices
  • The inherent challenges of production model deployment and how to overcome them
  • Model-monitoring best practices
  • Production lifecycle management and why it matters
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