From Development to Production: The Role of MLOps in Machine Learning Deployment

Abstract

Recently, data science operations have focused on ML model application in production systems, creating Machine Learning Operations. MLOps optimises ML model development, deployment, and upkeep. MLOps boosts production ML model deployment reliability, efficiency, and scalability.
ML process control is hard, hence MLOps was invented. ML model-specific CI/CD lets MLOps ship models iteratively. Model integration, testing, and deployment are simplified by ML CI/CD, speeding market entrance. We cover ML CI/CD and workflow automation best practices. ML model versioning is another MLOps notion. Successful versioning allows model rollback and replication. Analyzing metadata, model registries, and versioning effects on model governance and auditability.
Model monitoring and governance are MLOps. Monitor operational metrics, model performance indicators, and system health for model drift, performance degradation, and system failures. Model dependability and operational conformance are examined.
MLOps models suffer from data distribution changes and model drift. We diagnose and mitigate model drift via retraining and adaptive models that react to data trends. Analyze model reproducibility and data scientist-operations team integration.
Practical MLOps uses examples from several fields. Company MLOps case studies improve model dependability, scalability, and efficiency. The study compares MLOps' banking, healthcare, and retail strengths and downsides. The paper says MLOps' future trends and technology will help the sector. On cloud-native, containerization, and orchestration with AutoML and MLOps. MLOps approaches improve ML model deployment management by improving model performance and efficiency. Understanding MLOps' basics, issues, and applications helps us grasp how these approaches may conduct effective and scalable ML operations in production.