This course looks at the Amazon Forecast service, including what it does and how it works importing datasets, training predictors, and generating forecasts.
Learning Objectives
- Learn the fundamentals of Amazon Forecast
- Understand how to ingest data in Amazon Forecast
- Learn how to train the predictor model
- Learn how to create a forecast
Intended Audience
This course is intended for architects, developers, line of business managers, executives, and data scientists looking to improve their forecasting results in their business.
Prerequisites
In order to get the most out of this course, you will need to meet the requirements for the AWS Cloud Practitioner Certification.
With an existing trained predictor, our final step is to create a new forecast and perform a forecast lookup. Creating a new forecast takes a name, the predictor, an optional forecast types up to five quantile values. By default, forecasts will be generated for P10, P50 and P90 quantiles. The forecast dashboard will list all forecasts that belong to the dataset group that was selected. You can create and manage notifications for forecast events using Amazon EventBridge.
Also, when you create a forecast, Amazon Forecast generates forecasts for each unique item in your target time series data set. You can use the forecast lookup to find your forecast. You can also create a forecast export to Comma Separated Values, or CSV, to integrate with SAP and Oracle Supply Chain. A forecast lookup takes a forecast name from the existing forecast list. It also takes the start date for the historical demand you want to view as well as the end date for the forecast that you want to view. A sample Final Chart for evaluation is displayed on the console.
At this point, it's all about analyzing your predictions and calibrating it to benefit your business. This has been an overview of Amazon Forecast.
Experienced in architecture and delivery of cloud-based solutions, the development, and delivery of technical training, defining requirements, use cases, and validating architectures for results. Excellent leadership, communication, and presentation skills with attention to details. Hands-on administration/development experience with the ability to mentor and train current & emerging technologies, (Cloud, ML, IoT, Microservices, Big Data & Analytics).