Current use cases for machine learning in retail and consumer goods

Introduction

Companies that sell products and buyers will see how to use the Machine Learning Project (ML) to improve customer service and performance. For example, the route Azure will help retail and retail customers to improve the experience of purchasing and ensure that the rug was produced product always available when, where and how to use the client. Read more by reading: Use consumer goods and shops: Increase the SKU testing range + learning machine.

Here are the usual use cases for ML's customers and retailers, along with resources to launch the use of ML in Azure.

8 ML usage cases to improve the service and provide performance, transaction and weight gain

1. Improvement of assets through various types of SKU + machine maker ensures that the equipment is available and the best products are always ready for purchase.

2. Counselling Equipment: Coordinator Coordinator's Advisor to update performance capacities and delivery services that can generate high income.

3. The image lookup allows the ability to search for customers, rich in the topic and user-centred.

4. Feelings can help companies upgrading their products and services with a better understanding of how the application affects consumers.

5. Detection of fraud to identify any defects and other errors that show offensive behaviour.

6. Pricing application using the cost to meet the needs of the customer by creating the demand for the price of the price and the business barrier to increasing the benefits.

7. Specific promotion improves client experience by providing relevant information and providing retailers with increased information about customer involvement.

8. Establish the client's circle to improve strategic decisions about the client's commitment and the cost of living.

Machine learning in 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 greater speed and efficiency. Here are some resources to help you get started.

• Azure Auto Learning services allow you to create, implement and manage machine learning and Artificial Intelligence models using any Python library or tool.

• Azure Data Science virtual machines are custom images of VMs in Azure, loaded with data science tools used to create intelligent applications for advanced analysis.

• Azure Machine Learning Studio that comes with many ready-to-use algorithms.

• Azure AI Gallery, which shows AI and ML algorithms and uses cases for them.

The following recommended steps

• Complete the quick start of Azure Machine Learning services.

• Read the case of use of consumer goods and retailers: optimization of inventory through SKU assortment + automatic learning. This explains how consumer brands can take advantage of Azure to create and avoid shortages, ensuring that shelves are available and products are always available.

Additional resources

• Download the cheat sheet of the machine learning algorithm to help choose an algorithm.

• What machine learning algorithm should I use? Read How to choose algorithms for Microsoft Azure Machine Learning to get help with this question.

• Large-scale automatic learning. AI / ML expert Paige Bailey takes you through the services available in Azure. See how to bring your predictive model to production, train dynamically online with streaming updates and add real-time data to your models from IoT sources. Paige covers Azure Data Bricks, AI Batch, KubeFlow + AKS, Stream Analytics and Event Hubs.

• Read Welcome to Machine Learning Server for an introduction to the Microsoft Machine Learning Server (formerly called "R Server").

• Review the Solution for Machine Learning Server templates to see the industry-specific templates, including one for retailers.

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