The Growing Role of MLOps in Machine Learning Deployment

As machine learning adoption increases, MLOps (Machine Learning Operations) is becoming essential for managing the full lifecycle of ML models. MLOps bridges the gap between data science and DevOps, ensuring scalability, reliability, and automation in …


This content originally appeared on DEV Community and was authored by Morris

As machine learning adoption increases, MLOps (Machine Learning Operations) is becoming essential for managing the full lifecycle of ML models. MLOps bridges the gap between data science and DevOps, ensuring scalability, reliability, and automation in model deployment.

Key MLOps tools like MLflow, Kubeflow, and TensorFlow Extended (TFX) help streamline model versioning, monitoring, and retraining. Companies are increasingly integrating MLOps to reduce model drift, improve reproducibility, and automate CI/CD pipelines for AI.


This content originally appeared on DEV Community and was authored by Morris


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