MLOps is a set of practices and tools designed to streamline the entire lifecycle of machine learning models—from development to deployment and ongoing maintenance. It integrates DevOps principles to ensure models are reliable, scalable, and efficient in production.
MLOps ensures that machine learning models are reliable, maintainable, and scalable in production. Core principles include automation, monitoring, reproducibility, collaboration, and adaptability to changing data environments.
Key Components
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Development:
- Establish seamless workflows for data preprocessing, feature engineering, Model Building, and model training.
- Goal: Accelerate model development while ensuring quality and reproducibility.
- See DS & ML Portal for reference workflows.
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Deployment:
- Deploy models to production environments with the necessary infrastructure.
- Ensure models can handle real-world data, workloads, and user requirements.
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Maintenance:
- Monitor model performance continuously using observability tools.
- Detect data drift or concept drift and retrain models as needed.
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Generalization and Robustness:
- Build models that generalize well to unseen data.
- Ensure robustness to noisy, incomplete, or unexpected inputs.
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Collaboration and Automation:
- Promote collaboration among data scientists, engineers, and operations teams.
- Automate repetitive tasks such as model training, evaluation, and deployment.
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Versioning and CI/CD: