How to stop shelving ML porjects and see steady returns on data science investments
ML Ops: Operationalizing Data Science
Machine learning and analytics models are built to be used, not shelved. But some estimate more than half of ML and analytics models don’t make it into production.
By monitoring and managing your ML deployments, you can avoid having to shelve your data science initiatives due to a lack of compliance or shifting goals.
Read this short e-book to learn how to operationalize data science by getting these tools into production—and stop wasting the time and resources you put into these models.