Mlflow
by bCloud LLC
Version 3.16.0 + Free with Support on Ubuntu 26.04
MLflow is an open-source platform for managing the complete machine learning lifecycle, from experimentation to deployment. It simplifies the process of tracking experiments, packaging code into reproducible projects, and managing and serving models in production environments, all within a unified framework.
Features of MLflow:
- Tracking experiments, including metrics, parameters, and artifacts.
- Packaging machine learning code into reusable projects for reproducibility.
- Managing and versioning machine learning models with the Model Registry.
- Serving machine learning models via a REST API with MLflow Serving.
- Integrates with popular machine learning libraries such as TensorFlow, PyTorch, and Scikit-learn.
To check MLflow version:
$sudo su
$cd /opt/mlflow
$source /opt/mlflow/venv/bin/activate
$mlflow --version
Disclaimer: MLflow is an independ
MLflow is an open-source platform for managing the complete machine learning lifecycle, from experimentation and model development to deployment. It provides tools for tracking experiments, managing models, storing artifacts, and serving models through a unified platform.
Features of MLflow:
- Tracks machine learning experiments, including parameters, metrics, runs, and artifacts.
- Provides a web-based user interface for viewing and managing experiments and runs.
- Manages and versions machine learning models using the Model Registry.
- Supports serving trained machine learning models for inference.
- Integrates with popular machine learning frameworks and libraries such as Scikit-learn, PyTorch, and TensorFlow.
To check MLflow version:
$sudo su
$cd /opt/mlflow
$source /opt/mlflow/venv/bin/activate
$mlflow --version
To access MLflow Web UI:
$cd /opt/mlflow
$source /opt/mlflow/venv/bin/activate
$mlflow server --host 0.0.0.0 --port 5000
Open the following URL in a web browser:
http://SERVER-IP:5000
Disclaimer: MLflow is an open-source project maintained by its contributors and community. References to third-party machine learning frameworks, libraries, and products are for compatibility and informational purposes only. MLflow is provided under its applicable open-source license, and users are responsible for reviewing licensing, security, data protection, and regulatory requirements before deploying it in production environments.