CPG

In this post, we walk through how to build the FoodSavr solution (fictitious name used for the purposes of this post) using Amazon Rekognition Custom Labels to detect the ingredients and generate personalized recipes using Anthropic’s Claude 3.0 on Amazon Bedrock. We demonstrate an end-to-end architecture where a user can upload an image of their fridge, and using the ingredients found there (detected by Amazon Rekognition), the solution will give them a list of recipes (generated by Amazon Bedrock). The architecture also recognizes missing ingredients and provides the user with a list of nearby grocery stores.

In this post, we show you how Amazon Web Services (AWS) helps in solving forecasting challenges by customizing machine learning (ML) models for forecasting. We dive into Amazon SageMaker Canvas and explain how SageMaker Canvas can solve forecasting challenges for retail and consumer packaged goods (CPG) enterprises.