Operationalizing Machine Learning

George Larionov - OpenText
Badr Ouali - OpenText

Operationalizing Machine Learning 60% of ML projects that make it to production have taken more than three months to deploy, and 30% have taken a year or more. Since data science is a cost center for organizations until those models are deployed, the need to shorten and streamline the process from ideation to production is essential. Does that mean adding another technology to bloated stacks? Does this mean data scientists can’t use their preferred tools? Keep your data science team productive by using Vertica’s extensive in-database machine learning library of functions that get ML projects into production fast. See the latest innovations in VerticaPy that make Vertica’s in-database ML even more accessible to Python and Jupyter users, and the new VerticaLab that makes getting started easier than ever.