MLOps & Deployment
MLflow, Docker, FastAPI, Streamlit, Prefect, feature stores, data pipelines, monitoring, ML business metrics, cloud deployment
1What is the primary role of MLflow in an MLOps workflow?
What is the primary role of MLflow in an MLOps workflow?
Answer
MLflow is an open-source platform that manages the complete ML model lifecycle: experiment tracking (metrics, parameters, artifacts), model packaging, centralized registry and deployment. This enables experiment reproducibility and standardized model versioning.
2Which command is used to log a parameter in MLflow?
Which command is used to log a parameter in MLflow?
Answer
The mlflow.log_param function records a hyperparameter (learning rate, epochs, batch size) associated with a run. These parameters are then visible in the MLflow UI and allow comparison of different training configurations.
3What is the difference between mlflow.log_metric and mlflow.log_param?
What is the difference between mlflow.log_metric and mlflow.log_param?
Answer
log_param records fixed values defined before training (hyperparameters like learning_rate, epochs), while log_metric records values that change during or after training (accuracy, loss). Metrics can be logged multiple times with different steps to create curves.
What is the main advantage of using Docker to deploy an ML model?
Why use a multi-stage Dockerfile for an ML application?
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