Last updated: Mar 04, 2025
You can analyze data and build machine learning solutions with tools that provide various levels of automation. The methods that you choose for working with data or models help you determine which tools best fit your needs.
Each tool has a specific, primary task. Some tools have capabilities for multiple types of tasks.
You can choose a tool in a project based on how much automation you want:
- Code editor tools: Use to write code in Python or R, all also with Spark.
- Graphical builder tools: Use menus and drag-and-drop functionality on a builder to visually program.
- Automated builder tools: Use to configure automated tasks that require limited user input.
| Tool | Primary task | Tool type | Work with data | Work with models |
|---|---|---|---|---|
| Data Refinery | Prepare and visualize data | Graphical builder | ✓ | |
| Visualizations | Build graphs to visualize data | Graphical builder | ✓ | |
| Synthetic Data Generator | Generate synthetic tabular data | Graphical builder | ✓ | |
| Prompt Lab | Experiment with foundation models and prompts | Graphical builder | ✓ | |
| Tuning Studio | Tune a foundation model to return output in a certain style or format | Graphical builder | ✓ | ✓ |
| Jupyter notebook editor | Work with data and models in Python or R notebooks | Code editor | ✓ | ✓ |
| Federated learning | Train ML models on distributed data | Code editor | ✓ | |
| RStudio IDE | Work with data and ML models in R | Code editor | ✓ | ✓ |
| SPSS Modeler | Build ML odels as a visual flow | Graphical builder | ✓ | ✓ |
| Decision Optimization | Solve optimization problems | Graphical builder, code editor | ✓ | ✓ |
| AutoAI tool | Build machine learning models automatically | Automated builder | ✓ | ✓ |
| Pipelines | Automate model lifecycle | Graphical builder | ✓ | ✓ |
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