Machine Learning Engineering for Production (MLOps)
E824066
Machine Learning Engineering for Production (MLOps) is a specialized online course that teaches how to design, deploy, and maintain scalable, reliable machine learning systems in real-world production environments.
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
online course
ⓘ
specialized course ⓘ |
| aimsTo |
bridge gap between ML research and production
ⓘ
improve reliability of ML deployments ⓘ improve scalability of ML systems ⓘ prepare learners to run ML in production ⓘ teach best practices in MLOps ⓘ |
| context | real-world production environments ⓘ |
| covers |
automation in ML workflows
ⓘ
lifecycle management of ML models ⓘ operationalization of ML models ⓘ |
| deliveryMode | online ⓘ |
| field | MLOps NERFINISHED ⓘ |
| focusesOn |
deployment of ML models
ⓘ
design of ML production systems ⓘ machine learning systems in production ⓘ maintenance of ML systems ⓘ real-world production environments ⓘ reliable ML services ⓘ scalable ML infrastructure ⓘ |
| targetAudience |
data scientists
ⓘ
machine learning engineers ⓘ software engineers working with ML ⓘ |
| teaches |
CI/CD for ML systems
ⓘ
ML system design patterns ⓘ ML system reliability ⓘ collaboration between data scientists and engineers ⓘ concept drift detection ⓘ data drift detection ⓘ data pipelines for ML ⓘ end-to-end ML production lifecycle ⓘ experiment tracking ⓘ feature engineering in production ⓘ feature stores ⓘ governance of ML models ⓘ model deployment strategies ⓘ model performance monitoring ⓘ model retraining workflows ⓘ model versioning ⓘ monitoring of ML models ⓘ observability for ML ⓘ orchestration of ML pipelines ⓘ reproducibility in ML ⓘ resource management for ML workloads ⓘ scalability of ML services ⓘ security considerations for ML systems ⓘ testing of ML systems ⓘ |
Referenced by (1)
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