Demystifying MLOps: Operationalizing AI at Scale

Gartner predicts that by 2024, 75% of organizations will move from piloting to operationalizing AI and ML. Historically there has been a lot of emphasis on Model building while ignoring various other aspects within the model life cycle leading to less ML projects actually getting into production. ML Ops comprises tools, technologies, and practices to enable organizations to deploy, monitor, and govern machine learning models and other analytical models in production applications. Login to this session as Sateesh talks about how integrated MLOps addresses some of the challenges with current ML, its relationship with the data science ecosystem, the different MLOps maturity levels and how organizations are getting benefitted from integrated MLOps.