This blog was co-authored by Marty Donovan.

Retail and consumer goods companies are seeing the applicability of machine learning (ML) to drive improvements in customer service and operational efficiency. For example, the Azure cloud is helping retail and consumer brands improve the shopping experience by ensuring shelves are stocked and product is always available when, where and how the consumer wants to shop. Learn more by reading Retail and consumer goods use case: Inventory optimization through SKU assortment + machine learning.

Here are common use cases for ML in retail and consumer goods, along with resources for getting started with ML in Azure.

8 ML use cases to improve service and provide benefits of optimization, automation and scale

All of these use cases can be addressed using machine learning.

Machine learning on Azure

Customers can build artificial intelligence (AI) applications that intelligently process and act on data, often in near real time. This helps organizations achieve more through increased speed and efficiency. Here are some resources to help you get started.

Azure Machine Learning services enable building, deploying, and managing machine learning and AI models using any Python tools and libraries.

Azure Data Science Virtual Machines are customized VM images on Azure, loaded with data science tools used to build intelligent applications for advanced analytics.

Azure Machine Learning Studio which comes with many algorithms out of the box.

Azure AI Gallery, which showcases AI and ML algorithms and use cases for them.

Recommended next steps

Complete the Azure Machine Learning services quickstart.

Read Retail and consumer goods use case: Inventory optimization through SKU assortment + machine learning. This explains how consumer brands can leverage Azure to create to prevent stock-outs, ensuring shelves are stocked and products are always available.

Additional resources

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