What is MLOps architecture in Python machine learning?
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What is MLOps architecture in Python machine learning?
MLOps architecture refers to the practices and tools that enable collaboration between data scientists and operations teams to automate and streamline the deployment, monitoring, and management of machine learning models in production.
What are the key components of an MLOps architecture?
Key components include data versioning, model training pipelines, CI/CD for ML, monitoring, and governance frameworks.
How does MLOps improve the machine learning lifecycle?
MLOps enhances collaboration, reduces deployment time, ensures model reliability, and facilitates continuous integration and delivery.
Can you give an example of a tool used in MLOps?
Sure! Tools like MLflow, Kubeflow, and TensorFlow Extended (TFX) are commonly used for managing the MLOps lifecycle.
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