Summary and Conclusion

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

Enterprises across a broad spectrum of domains have identified artificial intelligence and machine learning use cases. Many even have multiple models deployed in production. Despite the progress, few have realized true value from their investments. The highly manual ad-hoc processes for model deployment drive up costs and impose a significant bottleneck in efforts to scale machine learning enterprise value.

Just as DevOps has revolutionized enterprise software development in the past decade, MLOps provides a culture and set of principles to deploy models in a reliable, scalable way. The practice shares many automation goals in common with DevOps but responds to the unique challenges inherent to machine learning systems.

Despite the widespread commoditization of algorithms, operationalizing models through MLOps practices represents a true competitive differentiator, especially as large organizations struggle to scale their prediction services. Enterprises that prioritize achieving these deployment competencies can realize a sustainable advantage in their industry while others remain stuck running science experiments.