Creating a robust and well-structured dataset is one of the most critical steps in training an effective machine learning model. With the AML Dashboard, users can seamlessly manage datasets for training AML models. This article provides a step-by-step guide to creating and using custom datasets with the AML Dashboard, focusing on their preparation and integration.
The AML Dashboard is a web-based tool that allows users to interact with the AML framework. Designed for developers looking to implement and test their own Machine Learning (AML) models, it provides an intuitive interface and advanced tools for model evaluation, fine-tuning, and validation across various scenarios. With this dashboard, users can monitor model performance, make real-time adjustments, and optimize deployment in production environments.
This tutorial demonstrates how to leverage AML-IP to parallelize the training of an Algebraic Machine Learning (AML) model within a distributed computing environment. AML-IP enables seamless data exchange between AML nodes, allowing users to create and manage clusters for concurrent training tasks.
The guide provides a step-by-step approach to setting up Main Nodes and Computing Nodes, distributing workload efficiently, and processing inference results. Additionally, it covers dataset loading, AML model training, and result handling. This tutorial serves as a foundation for implementing scalable AML training using AML-IP.
We are excited to introduce a new tutorial that guides users through sending data to the Context Broker using the AML Dashboard. This web-based tool enables seamless interaction with the AML framework, allowing users to efficiently upload data, create Fiware Nodes, and retrieve inference results.
This step-by-step guide will teach you how to set up the necessary components, send image data to the Context Broker, and obtain inference solutions. Additionally, we provide troubleshooting tips and insights into upcoming features, such as workload distribution for MNIST binary classification.
Read the full tutorial below and start exploring the capabilities of the AML Dashboard today
This demo shows aCollaborative Learning Scenario and the AML-IP nodes involved:Model Manager Receiver Node andModel Manager Sender Node. With these 2 nodes implemented, the user can deploy as many nodes of each kind as desired and check the behavior of a simulated AML-IP network running. They are implemented in Python to prove the communication between the two implementations.
The ALMA project leverages AML properties to develop a new generation of interactive, human-centered machine learning systems.
Plaza de la Encina 10-11, Núcleo 4, 2ª Pl.
28760 Tres cantos - Madrid (España)
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952091.
