Today businesses from fast paced startups to large enterprises and traditional businesses generate huge amount of sequential data points in unit of time. Organizations need mechanism to predict patterns and future time series data, looking at historical data to other variables. Machine learning can be used to forecast any time series data and serve use cases such as retail demand, manufacturing demand, travel demand, revenue and budget planning, IT capacity, logistics, price prediction, web traffic and more. In this session, we look at how developers can leverage and build an organization’s financial metrics (sales, expense, profits etc.) forecast solution with the help of Amazon Forecast and other AWS technologies. Learn how developers with no prior ML experience can build sophisticated forecasting model that uses machine learning to combine time series data and additional data variables.
AWS services: Amazon Forecast, AWS Lambda, AWS Step Functions, Amazon S3 , Amazon Athena, AWS Glue, Amazon QuickSight, Amazon API Gateway, Amazon Cognito, AWS Security Token Service, Amazon Identity and Access Management (IAM), Amazon Simple Notification Service (Amazon SNS)
Speaker: Darshit Vora, Startup Solutions Architect, AISPL
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