Why is it that 80% of enterprises fail to scale AI? Data scientists face operational, collaborative and infrastructure complexities at each step of the ML life cycle. MLOps practices have the ability to solve many ML operational concerns such as project deployment, testing, serving and monitoring.
In this webinar, Vasilis Vagias, AI architect at cnvrg.io, will discuss how MLOps solutions empower data scientists to successfully operationalize ML by applying DevOps principles to the ML life cycle.
We’ll answer the following key questions: