Leverage MLOps to scale AI/ML to the enterprise

Scaling the AI/ML process to the enterprise with MLOps

Artificial intelligence and machine learning (AI/ML) have spectacular potential to provide transformational insights. However, it’s often difficult to scale AI/ML models to the enterprise. In this episode, Mike Kavis and guest, Deloitte’s Sudi Bhattacharya, discuss the emerging discipline of MLOps and how it’s helping organizations develop sound models and then scale those to enterprise production—thus closing the “train to production” gap for AI/ML. According to Sudi, MLOps uses a three-step approach: continuously training the models, developing a robust deployment framework (including the right team), and integrating the AI/ML models into the business processes. One caveat: the approach to AI/ML should be problem first, technology later.

You have to think about building [models] for accuracy over time as well. So, that I think the right approach is problem first, technology–even if it’s AI/ML–later.

Sudi Bhattacharya is a managing director with Deloitte Consulting LLP in Deloitte’s Cloud Engineering practice. He leads big data and AI/ML driven business process transformation projects in public cloud environment for Fortune 500 businesses in finance, retail, CPG, and foodservice.

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