Faster iteration or tighter governance? An innovative AI/ML platform supports both.

Customers look to Thomson Reuters as a trusted source of content and technology. And with that understanding, the company has invested heavily in the use of automation to extract timely and important data that can help legal, tax, compliance, government, and media professionals make more confident decisions. These artificial intelligence/machine learning (AI/ML) models have been implemented across Thomson Reuters' enterprise and are driven by complex machine learning models that leverage state-of-the-art industry algorithms.

Maria Apazoglou, Thomson Reuters’ VP AI/ML & BI Platforms, had a deep understanding of the evolving AI/ML landscape and how she could apply it to help with customer success. She knew data science models can become vulnerable to performance issues (due to concept drift and data drift over time) and how imperative it becomes to assess model ethics like bias detection.

Businesses often combat this with performance modeling that requires individual data scientists to track the deterioration of models in production and then liaise with the relevant model governance teams to adjust. The number of models has grown significantly, as has the complexity of the problem statements they’re solving. That growth can increase business exposure and risk.

“As customers advance on the AI maturity curve, Deloitte observes that MLOps, ethics, explainable AI, and traceability for root-cause corrective action become focal areas for AI platform enhancements. The services we developed in collaboration with Thomson Reuters are significant steps in that direction,” noted Omer Sohail, principal, Deloitte Consulting LLP.

Apazoglou envisioned a single platform that could intentionally balance speed and governance, a platform where data scientists from across the enterprise could register specific models but leave performance tracking to an AI/ML monitoring system. The goal, she says, was to create a platform "flexible enough to accommodate different user personas, workflows, use cases, and types of models and at the same time standardize some core capabilities that then enable us to improve the way we govern, view, and trust our models."