Ease collaboration between data scientists and IT/Ops
Bringing machine learning in production is more difficult than just training ML models and deploying them as APIs for prediction. Only a small percentage of ML projects make it to production because of deployment complexity, lack of governance tools and many other reasons. Once in production, ML models often fail to adapt to the changes in the environment and its dynamic data which results in performance degradation.
To maintain the prediction accuracy of ML models in production, an active monitoring of model performance is mandatory. This allows to know when to retrain it using the most recent data and the newest implementation techniques, then redeploy in production.
To achieve this virtuous circle, an established CI/CD (continuous integration/continuous delivery), as well as continuous model training, suited for ML systems, is necessary. Deploying an ML pipeline that can automate the retraining and deployment of new models will help you adapt to rapid changes in your data and business environment.
Machine Learning Open Studio (MLOS) product from Activeeon helps data scientists and IT operations work together in an MLOps approach allowing to bring ML models to production. It simplifies machine learning application lifecycle management providing end-to-end orchestration, automation and scalability.
