According to Gartner, only 53% of machine learning proofs of concept (POC) are ever scaled to production. Even fewer manage to deliver the intended and measurable business value. On the bright side, Gartner projects that by 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5x increase in streaming data and analytics infrastructures.
Machine learning ops (ML Ops) is a practice and methodology for preparing, deploying and managing machine learning models in production. Productizing ML Ops is hard, but in reality, it’s a requirement for AI scalability and success.
In this Q&A session, Sapta Girisa, Senior Director at Lohika and Capgemini Engineering will discuss key considerations for ML Ops, why it’s essential to AI scalability and how to accelerate your AI engineering.
