- Le plus récent
- Le plus de votes
- La plupart des commentaires
The best starting place is the AWS Samples repo on GitHub:
https://github.com/aws-samples/amazon-forecast-samples
This is a great place to learn / show how to prepare a dataset, building a model and then evaluating a model's performance. It involves a CloudFormation template that creates some local resources, and then you dive into the provided Jupyter Notebook in order to work through all of the steps.
Variations of this have been run at Workshops at re:Invent and other events, so quickly looking back at session catalogs should show you who to contact.
The best understanding blog is the AWS machine learning blog.
With the Amazon Forecast Weather Index, you can now automatically include local weather information to your demand forecasts with one click and at no extra cost. The Weather Index combines multiple weather metrics from historical weather events and current forecasts at a given location to increase your demand forecast model accuracy. To get started with this capability, see Weather Index and go through the notebook in our GitHub repo that walks you through how to use the Forecast APIs to enable the Weather Index. You can use this capability in all Regions where Forecast is publicly available. For more information about Region availability, see AWS Regional Services.
Contenus pertinents
- demandé il y a un an
- demandé il y a 6 mois
- demandé il y a un mois
- AWS OFFICIELA mis à jour il y a 6 mois
- AWS OFFICIELA mis à jour il y a un an
- AWS OFFICIELA mis à jour il y a 2 ans