Variational Inference is a technique in machine learning and statistics used to approximate complex probability distributions. Instead of calculating exact distributions, which can be computationally expensive, it transforms the problem into an optimization task. By selecting a simpler family of distributions, it finds the best approximation to the true distribution by minimizing the difference between them. This method is particularly useful in Bayesian inference, where exact calculations can be intractable. Variational Inference allows for faster and more scalable solutions, making it suitable for large datasets and complex models, such as those found in deep learning and latent variable models.