1. High-level Framework vs. Library: Keras is a high-level neural networks API written in Python that can run on top of other deep learning frameworks like TensorFlow and Theano, whereas Pipelines is a specific feature in scikit-learn, a machine learning library in Python, used for creating machine learning workflows.
2. Focus on Deep Learning vs. Machine Learning: Keras is primarily focused on deep learning tasks such as building and training neural networks, while Pipelines in scikit-learn is designed for traditional machine learning algorithms like classification, regression, and clustering.
3. Flexibility and Customization: Keras provides a higher level of abstraction, making it easier to build and experiment with neural network architectures, whereas Pipelines in scikit-learn offer a more structured approach with predefined steps like data preprocessing, feature selection, and model fitting.
4. Community and Support: Keras has a large community of developers and researchers working on deep learning projects, providing a wide range of resources and support, while Pipelines in scikit-learn benefit from the broader ecosystem of scikit-learn with a wealth of machine learning algorithms and tools.
5. Deployment and Production: Keras models are often used in research and prototyping stages of deep learning projects, while models built using Pipelines in scikit-learn are more commonly deployed in production environments due to their focus on traditional machine learning tasks.
6. Ease of Use for Beginners: Keras is known for its simple and user-friendly interface, making it a popular choice for beginners in deep learning, whereas Pipelines in scikit-learn require a basic understanding of machine learning concepts and workflows, making it slightly more complex for newcomers.

In Summary, Keras and Pipelines in scikit-learn differ in their focus on deep learning versus machine learning, flexibility, community support, deployment scenarios, and ease of use for beginners.