This demo shows how to use Lemonade, a platform to create, test and run machine learning models, to create neural network models using Keras framework. Model building can be scheduled to run in a cloud infrastructure using Lemonade. Resource requirements, such as GPGPU support, can be defined in Lemonade and are allocated by Kubernetes, allowing scale Lemonade's modules to be scaled when demand increases. Finally, Lemonade provides a set of tools to help data scientists to test and evaluate model's results regarding properties such as privacy, stability, fairness and transparency.
Lemonade is being developed in the context of Atmosphere project. It is a tool that enables the integration between machine learning models and neural networks (application side) and cloud-enabled services (infrastructure). It provides state-of-art tools and algorithms related to machine learning, deep learning, big data and is organized as a scalable microservices architecture that fits very well to cloud schedulers such as Kubernetes.