In time series forecasting we are interested in how the time series is going to continue in the future. This is of high importance in areas like forecasting energy production from renewable resources, forecasting demand of customers or the price of products. Many forecasting algorithms provide only the prediction. However, oftentimes we are also interested in the likelihood of the prediction and how much it may vary. This is what probabilistic forecasting is for. With every forecast, we also obtain an upper and lower bound with certain probabilities. For a long time, probabilistic forecasting was limited to traditional techniques like ARIMA. DeepAR is an algorithm that allows us to combine Deep Learning techniques with probabilistic forecasting. Additionally, in contrast to training a model for each time series individually, DeepAR suggests training one large forecasting model for all related time series. The algorithm was developed by Amazon and is also provided in AWS SageMaker. In this talk, we will understand the theoretical basics of DeepAR, have a look at a practical time series example and will demonstrate an implementation. In the end, you will be prepared to get started with your own forecasts.