What is the difference between MLOps and DevOps?
An example of an AI chatbot conversation about MLOps and DevOps differences.
What is the difference between MLOps and DevOps?
MLOps focuses on machine learning model deployment and management, while DevOps is about software development and operations.
Can you explain how MLOps integrates with DevOps?
MLOps can be seen as an extension of DevOps, incorporating practices for managing ML lifecycle, including data, models, and experiments.
What are some tools used in MLOps?
Common MLOps tools include MLflow, Kubeflow, and TFX, which help in tracking experiments and deploying models.
Are there specific challenges in MLOps compared to DevOps?
Yes, MLOps faces unique challenges like data versioning, model drift, and the need for continuous retraining.
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