Amazon Personalize vs Greykite

Combine customer and contextual data to generate high-quality recommendations; Automated machine learning; Continuous learning to improve performance; Bring your own algorithms; Easily integrate with your existing tools;

Provides time series regressors to capture trend, seasonality, holidays, changepoints, and autoregression, and lets you add your own; Fits the forecast using a machine learning model of your choice; Provides powerful plotting tools to explore seasonality, interactions, changepoints, etc; Provides model templates (default parameters) that work well based on data characteristics and forecast requirements (e.g. daily long-term forecast); Produces interpretable output, with model summary to examine individual regressors, and component plots to visually inspect the combined effect of related regressors; Facilitates interactive prototyping, grid search, and benchmarking. Grid search is useful for model selection and semi-automatic forecasting of multiple metrics; Exposes multiple forecast algorithms in the same interface, making it easy to try algorithms from different libraries and compare results; The same pipeline provides preprocessing, cross-validation, backtest, forecast, and evaluation with any algorithm