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. It is designed to be extensible, so you can write plugins to support new workflows, libraries, and tools. The Jupyter Notebook is the original web application for creating and sharing computational documents. It offers a simple, streamlined, document-centric experience. Jupyter Notebook is also a valuable tool for sharing code and research. Jupyter Notebook documents can be easily shared with others, and they can be viewed and executed in a web browser. This makes it easy to collaborate with others on projects and to share your work with a wider audience.

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.

Key features of Jupyter Notebook:

• Rich text support: Jupyter Notebook documents can contain rich text elements, such as equations, images, and tables. This makes it easy to create documents that are both informative and engaging.
• Sharing: Jupyter Notebook documents can be easily shared with others, and they can be viewed and executed in a web browser. This makes it easy to collaborate with others on projects and to share your work with a wider audience.
• Open source.

To check the installation of MLflow and Jupyter Notebook, go through the steps given: 1. Open the windows command prompt as administrator. 2. Change the directory to C:\. 3. Activate the virtual environment by running the following command: myenv\Scripts\activate 4. Verify that above mentioned libraries are installed by running the following commands: i)pip show mlflow ii)pip show notebook If both the tools are installed correctly, you will see output that includes the version, location and dependencies for each tool.

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

Default ports:

• RDP: 3389
• HTTP: 80
• HTTPS: 443

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