Unity-based desktop application facilitates the preparation, analysis, and management of all ML data and models. Low to no-code ML development simplifies predictive analytics with graphical point-and-click capabilities. Federated ML development enables secure privacy-preserving ML training by deploying training to data sources, relaxing the need to collect data at a central point. Autoscaling distributed ML training enables easy development for large-scale datasets. Seamlessly integrated ML explainability helps in model debugging, revealing model risk, and increasing trust with application stakeholders. Integrated Jupiter Notebooks enable the development and management experiment and production code in one place. Granular level security controls (including data cell-level security) help support compliance with industry regulatory standards. Support for leading open-source ML Libraries (e.g., TensorFlow, PyTorch) enables users to choose “the right tool for the right job.”