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, tractable distribution, it seeks to minimize the difference between this approximation and the true distribution. This method is particularly useful in Bayesian inference, where it helps in estimating posterior distributions. Variational inference allows for faster computations and scalability, making it suitable for large datasets and complex models, such as those found in deep learning and latent variable models.