Thanks for your question. Yes, we did research the space a lot before making AutoAI. Here is what we found:
PyCaret: Semi-automatic. You do the first run; then you figure the next set of runs. Ensemble models require manual configuration.
Tpot: Does a great job. Generates 4-5 lines of py code too. But does not support Neural Networks / DNN. So works only for problems where GOFAI works.
H2O.ai: They have an open-source flavor, but the best way to use it is the enterprise version on the H2O cloud. The interface is confusing, and the final output is black-box.
Now there are many in the enterprise category, such as DataRobot, AWS SageMaker, Azure etc. Most are unaffordable to Data Scientists unless your employer is sponsoring the platform.
AutoAI: This is 100% automated. Uses GOFAI, Neural Networks and DNN, all in one box. It is 100% White-box. It is the only AutoML framework that generates high-quality (1000s of lines) of Jupyter Notebook code. You can check some example codes here: https://cloud.blobcity.com
PyCaret: Semi-automatic. You do the first run; then you figure the next set of runs. Ensemble models require manual configuration.
Tpot: Does a great job. Generates 4-5 lines of py code too. But does not support Neural Networks / DNN. So works only for problems where GOFAI works.
H2O.ai: They have an open-source flavor, but the best way to use it is the enterprise version on the H2O cloud. The interface is confusing, and the final output is black-box.
Now there are many in the enterprise category, such as DataRobot, AWS SageMaker, Azure etc. Most are unaffordable to Data Scientists unless your employer is sponsoring the platform.
AutoAI: This is 100% automated. Uses GOFAI, Neural Networks and DNN, all in one box. It is 100% White-box. It is the only AutoML framework that generates high-quality (1000s of lines) of Jupyter Notebook code. You can check some example codes here: https://cloud.blobcity.com