Adopting cnvrg.io delivers significant MLOps value to an organization as it accelerates the time to result for particularly data-driven problems:
- Faster experimentation and model development—Model optimization requires many tests with different model features and weights in parallel (also known as hyperparameter optimization). By streamlining and optimizing this process, MLOps shortens the cycle by empowering the data science team to train and test many models at once.
- Faster deployment of updated models into production—As noted above, only a small majority of ML experiments are deployed into production, and even then, the process takes an average of nine months. MLOps automation and reusability principles help to quicken training and deployment.
- Better quality assurance—cnvrg.io gives data scientists and ML engineers full visibility into model performance, drift and erratic behaviors, as well as impacts on cluster heath and resource consumption. Users can either completely automate the retraining of models as new data arrives or put humans in the decision loop when expert judgment is needed. Improving the quality of model performance improves the quality of the answers coming out of the data.