We have developed a user-friendly machine learning platform designed to make advanced AI technologies accessible to non-technical users. The platform allows users to effortlessly upload, annotate and manage their data while also enabling the creation of new, customized datasets. Users can choose from a wide range of machine learning models, including models capable of semantic segmentation, object detection and classification tasks to initiate training with just a few clicks. Upon completion of training, the platform automatically preserves the best-performing model among multiple trials, allowing seamless inference on new data. Every step of the workflow (data management, model selection, training and inference) is executed through an intuitive and responsive user interface, ensuring a seamless and efficient experience. Behind the scenes, the platform leverages Kubernetes-based technologies such as Kubeflow to manage the backend infrastructure, ensuring scalability, reliability and optimal resource allocation. With its modular architecture, the platform can easily accommodate new computer vision tasks, ensuring adaptability to a wide range of application fields. 

Github page: https://github.com/TEXTaiLES/AmalthAI/

Description: https://textailes.github.io/AmalthAI-documentation/