Stratascale Horizon Report Data Intelligence (Vol. 3) - Stratascale Horizon Report #10

CI: DevOps and MLOps both require source control unit testing and integration testing, but machine learning systems require data, data schemas, and model validation.
CD: DevOps typically focuses on a single software package, but CD in machine learning pipelines deploys an entire model prediction service.
Monitoring: Developers need to monitor inference data and model KPIs in addition to bugs and errors.
Continuous Training: In addition to CI/CD, machine learning requires continuous training to automate retraining and redeploying models. xxi

Stratascale Horizon Report Data Intelligence (Vol. 3)Driving Competitive Advantage with MLOpsIn this Horizon Report we explore how enterprises should integrate data models into their complex operating environments.(MLOps).Contents

Executive Summary

To borrow a baseball analogy, organizations can hit a home run with a one-off machine learning model but still lose the game if they don’t bring machine learning to scale. Enterprises must integrate their models into their complex operating environments. Specifically, technology and business leaders should use machine learning operations (MLOps) to improve the efficiency of data science projects and maintain the relevance of deployed assets.