MLflow is an open-source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components which are MLflow Tracking, MLflow Projects, MLflow Models, Model Registry. MLflow is library agnostic. You can use it with any machine learning library, and in any programming language, since all functions are accessible through a REST API and CLI. For convenience, the project also includes a Python API, R API, and Java API. It has built-in integrations with many popular ML libraries, but can be used with any library, algorithm, or deployment tool. It is designed to be extensible, so you can write plugins to support new workflows, libraries, and tools.

Key features of MLflow:

• Tracking: MLflow allows you to track your ML experiments by logging parameters, metrics, and artifacts. This data can then be used to visualize and compare runs, identify the best models, and reproduce experiments.
• Packaging: MLflow provides a standard format for packaging ML models. This makes it easy to share and deploy models to different environments.
• Deployment: MLflow includes a number of tools for deploying ML models to production, including a model registry, a REST API, and a CLI.
• Open source.

 Tested by the Apps4rent engineers, MLflow on Windows Server 2019 is supported by Azure cloud infrastructure precisely. In case you have any issues, our Azure experts are available 24/7 via phone, chat, and email. Get in touch today!

Disclaimer: Apps4Rent does not offer commercial licenses of any of the products mentioned above. The products come with open-source licenses. 

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• RDP: 3389
• HTTP: 80
• HTTPS: 443

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