PyCaret

PyCaret's automated workflows may restrict fine-tuning of model parameters and preprocessing steps, which can be critical for achieving optimal performance in complex use cases.

The library may struggle with memory management when handling very large datasets, leading to slower processing times or out-of-memory errors.

Compared to more established ML libraries like scikit-learn or TensorFlow, PyCaret has a smaller user base which can result in fewer third-party contributions and slower response times for support issues.

While the documentation is beginner-friendly, it may not provide enough detail on underlying algorithms or best practices for more experienced practitioners who need to understand the nuances of each model and preprocessing step.