1
Install MLflow
Install MLflow via pip using the command `pip install mlflow`.
2
Run MLflow UI
Start the MLflow tracking UI locally by running `mlflow ui`.
3
Log an Experiment
Use MLflow tracking APIs in your Python script to log parameters, metrics, and artifacts (e.g., `mlflow.log_param`, `mlflow.log_metric`).
4
Package and Register a Model
Package your model using MLflow's model format and register it in the model registry.
5
Deploy the Model
Deploy the model via integrations such as AWS SageMaker, Databricks, or self-hosted endpoints.