Is MLOps Right for My Organization?
Organizations with multiple data science teams creating and attempting to deploy machine learning models need MLOps. Even if some models are running in production already, custom coding and manual processes cause roadblocks. Scaling is difficult, and DataRobot can help organizations build a governance strategy to get those models off the ground.
- Organizations that are just getting started (<1-5 models in production). Data scientists who are frustrated with having to manually deploy, monitor and replace models by following IT processes that are not designed for machine learning.
- Organizations that are attempting to create processes for machine learning in production. Data science, data engineering, architects or IT personas tasked with improving processes for deploying, monitoring, and managing models in production.
- Mature organizations that have processes for updating models in production. Data science, IT, and MLOps leaders who are seeking a solution for increasing the speed and capacity of MLOps, often motivated by an executive who is frustrated that it takes way too long to deploy and replace a model.
I really think using DataRobot MLOps is the reason why we didn’t have to stress about it [COVID] as much as other companies have. The only reason we were comfortable in doing that is that when we see performance changes via MLOps we can throw everything automatically back into DataRobot AutoML and see what it tells us in terms of model comparison and see what we need to do based on where we’re at at that point of time.