Let’s dive into a hands-on example of using Amazon Forecast to build a sales forecasting model. In this example, you’ll predict future sales based on historical data.
Set up your dataset: Prepare a dataset containing historical sales data, ensuring it includes relevant timestamps and corresponding sales figures.
Create a dataset group: Use the Amazon Forecast console or API to create a dataset group, grouping related datasets for forecasting.
Import your data: Upload your historical sales dataset to Amazon Forecast, allowing the service to learn patterns from the provided data.
Train your model: Initiate model training using the Forecast console or API. Amazon Forecast will automatically select suitable algorithms and optimize hyperparameters.
Generate forecasts: Once the model is trained, generate forecasts for future sales based on the patterns identified in your historical data.
By leveraging advanced features and implementing optimization strategies, you can elevate your Amazon Forecast experience. The flexibility and adaptability of the service allow you to tailor forecasting solutions to the specific needs of your business. For example, you can improve the precision of your forecasts by integrating external variables. Amazon Forecast allows you to include additional information, such as promotions, holidays, or economic indicators, that might impact the time series you are forecasting. By considering these external factors, your models can adapt to changing circumstances and provide more nuanced predictions.
In this chapter, you learned about a few of the AWS AI services that can be used to solve various problems. You used the Amazon Rekognition service, which detects objects and faces (including celebrity faces), and can also extract text from images. For text to speech, you used Amazon Polly, while for speech to text, you used Amazon Transcribe. Toward the end of this chapter, you built a chatbot in Amazon Lex and learned the usage and benefits of Amazon Forecast.
For language detection and translation in an image, you used Amazon Rekognition, Amazon Comprehend, and Amazon Translate. You learned how to combine all of them into one Lambda function to solve our problem.
For the certification exam, you do not need to remember all the APIs you used in this chapter. There may be questions on a few of the best practices that you learned or on the names of services that solve a specific problem. It is always good to practice using these AWS AI services as it will enhance your architecting skills.
In the next chapter, you will learn about data preparation and transformation, which is the most important aspect of machine learning.
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