Cloud-Native AI/ML Pipelines: Best Practices for Continuous Integration, Deployment, and Monitoring in Enterprise Applications
Keywords:
Cloud-native AI/ML pipelines, continuous integrationAbstract
Data-driven decision-making, automation, and innovation made possible by artificial intelligence (AI/ML) has changed business applications. Including artificial intelligence and machine learning models into production requires for robust CI/CD/CM infrastructure and procedures to verify scalability, regulatory compliance, and model accuracy. Corporate continuous integration, deployment, and best practices are the main focus of this cloud-native AI/ML pipeline design and implementation study. On scaled systems, containerizing, microservices, serverless computing, IaC enable rapid development and deployment practical. With recommendations for version control systems, Kubernetes, model serving frameworks, and continuous monitoring of cloud-native artificial intelligence/machine learning pipelines, this paper CI/CD automates monitoring, validation, deployment, and model training.
For AI/ML model lifecycle management, integration, and use cases Jenkins, GitLab CI, Tekton, Kubeflow, MLflow, and Seldon are examined. Examined additionally include model versions, drift detection, data governance, repeatability in cloud-native AI/ML pipeline orchestration. It drives ModelOps in data science, DevOps, and IT operations across teams to maximize business goals and simplify production. According to the paper, in highly regulated settings AI/ML models must be interpretable, fair, GDPR and CCPA compatible.
Apart from AWS and GCP, the paper looks at Azure's AI/ML and CI/CD technologies. This study leads companies toward secure, scalable, compliance, cloud solutions. The article addresses Infrastructure as Code (IaC) solutions such Terraform and AWS CloudFormation to automate cloud resource provisioning, guarantee consistency across environments, and reduce configuration drifts. Combining public and private clouds allows one to protect data, reduce costs, and reconstruct after a disaster.
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