Uber is providing a fleet of small vehicles to help the UN deliver emergency food and water supplies to war-torn areas of Ukraine. As part of a collaboration with the UN's World Food Programme (WFP), Uber is using a custom-built version of its platform for relief efforts. Larger vehicles face issues reaching in-need Ukrainians in built-up areas, such as structural damage and high threat of Russian bombardment. So the initiative is using a fleet of smaller vehicles such as vans to send relief items from warehouses to people in need in densely populated parts of the country. Just like any ride on the Uber app, the progress of deliveries can be tracked in real-time through a special'private-label' version of its app platform.

We're excited to announce that you can now measure the accuracy of your forecasting model to optimize the trade-offs between under-forecasting and over-forecasting costs, giving you flexibility in experimentation. Costs associated with under-forecasting and over-forecasting differ. Generally, over-forecasting leads to high inventory carrying costs and waste, whereas under-forecasting leads to stock-outs, unmet demand, and missed revenue opportunities. Amazon Forecast allows you to optimize these costs for your business objective by providing an average forecast as well as a distribution of forecasts that captures variability of demand from a minimum to maximum value. With this launch, Forecast now provides accuracy metrics for multiple distribution points when training a model, allowing you to quickly optimize for under-forecasting and over-forecasting without the need to manually calculate metrics.