The paper introduces stochastic variational inference (SVI) for Gaussian process (GP) models, enabling their application to large datasets with millions of data points. The authors show how GPs can be decomposed using inducing variables to factorize the model, facilitating variational inference. This approach is extended to models with non-Gaussian likelihoods and latent variable models based on GPs. The method is demonstrated on a toy problem and two real-world datasets: UK apartment price data and airline delay data. The results show that SVI can handle large datasets efficiently, allowing for richer models with more inducing variables. The paper also discusses the computational complexity and the ability to handle multiple outputs, making the method suitable for various applications.The paper introduces stochastic variational inference (SVI) for Gaussian process (GP) models, enabling their application to large datasets with millions of data points. The authors show how GPs can be decomposed using inducing variables to factorize the model, facilitating variational inference. This approach is extended to models with non-Gaussian likelihoods and latent variable models based on GPs. The method is demonstrated on a toy problem and two real-world datasets: UK apartment price data and airline delay data. The results show that SVI can handle large datasets efficiently, allowing for richer models with more inducing variables. The paper also discusses the computational complexity and the ability to handle multiple outputs, making the method suitable for various applications.