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AI Learning Organizer (ALO)

Updated 2025.03.19

What is AI Learning Organizer (ALO)?​

AI Learning Organizer (ALO) is a specialized framework designed to make AI Solution development more efficient and streamlined. ALO enables users to handle AI model development, training, testing, and deployment as a unified, integrated process.

Following ALO’s guidance, users can develop the necessary assets and link them into a single machine learning pipeline (ML Pipeline) for training and testing. Once completed, the AI Solution is containerized so it can leverage the full range of Mellerikat’s resources. Through this containerization process, the AI Solution can be registered with Mellerikat. From there, various users can easily optimize the model with their own data, request training, and deploy results to edge devices.

AI Solutions developed with ALO are powerful tools that offer high efficiency and flexibility to meet diverse business needs. By managing every aspect of AI Solution development in an integrated manner, ALO helps users more easily create and operate high-quality AI models.


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Key Features​

ALO provides the following core functionalities:

Simplified Development​

ALO streamlines the AI model development and training process, enabling users to work more efficiently.

Integrated Pipelines​

Multiple assets can be unified into a single machine learning pipeline, allowing for consistent management of the training and testing workflow.

Optimized Resource Utilization​

Trained AI Solutions are containerized to fully leverage Mellerikat’s resources.

Easy Deployment and Optimization​

Users can easily tailor AI Solutions to their own environments and data, and deploy them to edge devices.


User Scenario​

Typical ALO user scenarios include:

  1. Installation and Setup: The data scientist installs ALO on their local PC, server, or cloud infrastructure to prepare the AI model development environment.

  2. Develop AI Solution: The user develops an AI Solution using ALO. They may design the solution based on AI Contents or build it entirely from scratch.

  3. Register AI Solution: The developed AI Solution is registered to AI Conductor via ALO. At registration, a training instance is automatically assigned.

  4. Testing and Validation: ALO’s testing module is used to verify that the AI Solution trains successfully on the assigned instance.

  5. Training Completion and Deployment Preparation: Once training is complete, the results are displayed in AI Conductor, finalizing the AI Solution registration. From there, users can initiate training requests via Edge Conductor and deploy inference to Edge App.


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