Main Purpose
IBM Watson Studio empowers data scientists, developers, and analysts to build, run, and manage AI models, and optimize decisions anywhere on IBM Cloud Pak for Data. It provides a collaborative environment for data science and AI development, offering a range of tools and capabilities to streamline the end-to-end AI lifecycle.
Key Features
- Integration with open-source frameworks like PyTorch, TensorFlow, and scikit-learn
- Support for code-based and visual data science using Jupyter notebooks, JupyterLab, and CLIs
- Multi-language support including Python, R, and Scala
- Automation of AI lifecycles and deployment on an open multicloud architecture
- Collaboration features to unite teams and facilitate knowledge sharing
- Tools for data preparation, analysis, modeling, and visualization
- AutoAI capabilities for beginners and expert data scientists
- Decision Optimization for prescriptive modeling and scenario comparison
- SPSS Modeler for building predictive models using a graphical canvas
- Federated Learning for training models on distributed data sources while maintaining data security
Use Case
- Data science and AI model development and deployment
- Collaborative data analysis and modeling
- Automated model development and hyperparameter optimization
- Prescriptive modeling and decision optimization
- Predictive modeling and data preparation
- Training models on federated data sources
