Different usages of Machine Learning Open Studio
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The document outlines features of a machine learning open studio designed for a diverse range of users, including data scientists, AI architects, and beginners. It highlights capabilities such as data cleaning, transformation, model training, prediction, and visualization, along with tools for building, optimizing, and managing ML pipelines. Additionally, it mentions automation dashboards and resource management for deploying models as services and monitoring their lifecycle.
Different usages of Machine Learning Open Studio
- 1. Machine Learning Open Studio Users ML Studio (generic tasks) Data Analytics Data scientists AI ArchitectsNon AI Engineers ML Beginners ML Experts - Data cleaning - Data transformation - Model training - Prediction - Data visualization Jupyter Kernel + AutoML + Job Analytics - Build your pipeline locally using your favorite kernel - Transform and Scale your pipeline using Proactive kernel - Optimize your model hyperparameters using AutoML - Evaluate and compare your different models using Job Analytics Automation Dashboard + Resources Manager - Deploy your Model as a Service and manage its lifecycle - Schedule and monitor end to end ML pipelines - Manage and select the resources adapted for each pipeline task