You want to scale your use of AI, but you are blocked by production issues. Which means that data scientists have to help deploy and maintain their models, which is costly and takes away from doing new data science.
What if there was a better way? Machine Learning Operations (MLOps) will get your AI projects out of the lab and into production where they can generate value and help transform your business.
In this 15-minute podcast produced by Data Science Central, we explore best practices in Production Model Monitoring. With MLOps, your monitoring is designed for machine learning. Monitoring includes service health, data drift, model accuracy, and proactive alerts that are sent to stakeholders using a variety of channels like email, Slack and Pagerduty, based on severity. With MLOps monitoring in place, your teams can deploy and manage thousands of models, and your business will be ready to scale production AI.