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Why isn’t your AI delivering ROI?

How to bridge the production gap between data and IT

Artificial Intelligence (AI) has become a key component of companies’ bids to modernise in recent years. 

Organisations have been scrambling to get an advantage over their competitors and see how greater volumes of data insights can give them an edge.

However, introducing AI without having a full understanding of where it will fit in your strategy can mean your investment won’t generate a return. But one thing’s for certain, that once it’s implemented, you need to run with it, as Gartner predicts that by 2024, 75% of organisations will initiate some form of machine learning (ML).

However, data scientists shouldn’t be extracting deep insights from company data unless they’re aligned to a business goal or solving a business problem. This can then indicate how AI and machine learning can be attributed to success.

Sivan Metzger, managing director, MLOps & Governance at DataRobot, has said that we’re now at a point where businesses will finally start to see the benefit of their AI investments, as pressure from CFOs continues to mount. 

He goes on to say that data scientists should also begin to see results that can help them to adapt their work to give their company a better advantage.

Previously, this had been an issue, and 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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