1. Integration with deep learning: Ludwig is a deep learning toolbox that allows for the incorporation of deep learning models into traditional machine learning pipelines seamlessly. In contrast, scikit-learn focuses primarily on conventional machine learning algorithms and does not have built-in support for deep learning models.

2. Customizability: Ludwig offers a high degree of customizability, allowing users to define and train complex models with minimal coding requirements. On the other hand, scikit-learn provides a more limited level of customization, with pre-defined algorithms and parameters being the primary options for modeling.

3. Ease of use for non-experts: Ludwig is designed to be user-friendly and accessible to those without an extensive background in deep learning or machine learning. It provides an intuitive interface for building, training, and evaluating models. Scikit-learn, while user-friendly, may require a greater understanding of machine learning concepts and parameters for optimal usage.

4. Multi-task learning capabilities: Ludwig supports multi-task learning, enabling the development of models that can handle multiple related tasks simultaneously. This feature is not as comprehensive in scikit-learn, where the primary focus is on single-task learning.

5. Model interpretability: Ludwig offers better model interpretability tools, such as visualization and analysis of model predictions, making it easier to understand how the model arrives at its decisions. Scikit-learn also provides some interpretability tools but may not be as extensive as Ludwig's capabilities.

6. Deployment options: Ludwig provides more straightforward deployment options for putting models into production, including conversion to various formats for serving in different environments. In contrast, scikit-learn might require additional steps or third-party tools for deployment in production settings.

In Summary, Ludwig and scikit-learn differ in their integration with deep learning, customizability, ease of use for non-experts, multi-task learning capabilities, model interpretability, and deployment options.