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19 lines
1.5 KiB
19 lines
1.5 KiB
# MLOps Principles |
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MLOps (Machine Learning Operations) principles focus on streamlining the deployment, monitoring, and management of machine learning models in production environments. Key principles include: |
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1. **Collaboration**: Foster collaboration between data scientists, developers, and operations teams to ensure alignment on model goals, performance, and lifecycle management. |
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2. **Automation**: Automate workflows for model training, testing, deployment, and monitoring to enhance efficiency, reduce errors, and speed up the development lifecycle. |
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3. **Version Control**: Implement version control for both code and data to track changes, reproduce experiments, and maintain model lineage. |
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4. **Continuous Integration and Deployment (CI/CD)**: Establish CI/CD pipelines tailored for machine learning to facilitate rapid model iteration and deployment. |
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5. **Monitoring and Governance**: Continuously monitor model performance and data drift in production to ensure models remain effective and compliant with regulatory requirements. |
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6. **Scalability**: Design systems that can scale to handle varying workloads and accommodate changes in data volume and complexity. |
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7. **Reproducibility**: Ensure that experiments can be reliably reproduced by standardizing environments and workflows, making it easier to validate and iterate on models. |
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These principles help organizations efficiently manage the lifecycle of machine learning models, from development to deployment and beyond. |