Differential Privacy interfaces with machine learning and data analysis by providing a framework to use sensitive data safely. It's especially relevant in training machine learning models where data privacy is a concern. For example, in deep learning, Differential Privacy can be integrated into training algorithms like stochastic gradient descent. By adding noise to each training iteration, the algorithm becomes differentially private, ensuring that individual data points in the training set do not unduly influence the model's output. This approach has been adopted in differential private versions of popular machine learning frameworks like TensorFlow, developed by Google. Such integration showcases how Differential Privacy can be applied in complex data analysis scenarios while preserving individual privacy.