Brief on MLflow
Whether you are building your first machine learning model or managing dozens in production, MLflow is the tool that help you track, reproduce, and manage your ML lifecycle like a pro.
In this blog, I’ll walk you through what MLflow is, why it matters, and how you can start using it to supercharge your ML workflow — especially if you’re already in the DevOps or MLOps world!
🤖 So what is MLflow?
MLflow is an open-source platform created by Databricks to manage the end-to-end machine learning lifecycle. Think of it as GitHub + CI/CD pipelines, but for ML models.
MLflow helps you with:
- Experiment Tracking
- Model Packaging
- Model Registry
- Model Deployment
💻 MLflow Components
There are four components of Mlflow –
1. MLflow Tracking
You can logs and tracks experiments:
- Parameters: learning rate, number of layers...
- Metrics: accuracy, loss ..
- Artifacts: models, plots, or preprocessing files
- Source Code: used in experiment
import mlflow
with mlflow.start_run():
mlflow.log_param("learning_rate", 0.01)
mlflow.log_metric("accuracy", 0.94)
mlflow.log_artifact("confusion_matrix.png")
2. MLflow Projects
Standardize your code using MLproject files. This makes your experiments portable and reproducible anywhere — whether locally, on a VM, or in the clou.
3. Model Registry
A centralized store to:
- Register new models,
- Transition models between stages (Staging -> Production -> Archieved),
- Version control for models
Think of it like "GitHub for models" with versioning and approval workflows.
4. MLflow Models
Package your trained models in a standard format so they can be deployed to:
- AWS SageMaker
- Azure ML
- Docker
- or even as a simple REST API!
You can load and serve your model like this:
mlflow models serve -m runs:/<run-id>/model
In short, its your control center for all things machine learning needed.