In the MLJAR Studio user creates Python notebook with help of visual interface for code generation. The notebooks created with MLJAR Studio are compatible with Jupyter Notebooks format.
There are several steps with predefined User Interface for code generation. The steps in the MLJAR Studio version 1.0.0 are focused on Machine Learning. You can easily built a Machine Learning model for tabular data with them. See the steps in the GIF below.
The steps list will be extended in the near future to offer more steps for automation. Thanks to unique approach for code generation anyone will be able to create Python scripts/programs/analysis. The created notebooks are stored locally, because MLJAR Studio is a desktop application.
The list of code cells define a Python script. The cells are executed sequentially. Such approach is an alternative to node-based visual programming environments. The advantage of our solution is that Python code is the only source of truth. It can be easily extended and edit, because MLJAR Studio accepts plain Python (it works with Markdown as well).
The example step with reading CSV file to Pandas DataFrame.
The example step displaying the Pandas DataFrame.
Please take a look at our roadmap to see what are we planning to add in the application.