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Landmark Recognition With Machine Learning

Ever seen a breathtaking landmark or scenery while flipping through a book or magazine and been frustrated because you don't know what it's called or where it is? Wouldn't it be great if there was an app that could tell you what you're seeing! Fortunately, machine learning makes it remarkably easy to develop such an app.  

Introduction to Landmark Recognition

The landmark recognition service enables you to obtain the landmark name, landmark longitude, and latitude, and even a confidence value of the input image. When you input an image for recognition, a confidence value will be provided whereby a higher confidence value indicates that the landmark in the input image is more likely to be recognized. You can then use this information to create a highly-personalized experience for your users. 

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Apache Kafka and Machine Learning in Pharma and Life Sciences Industry

This blog post covers use cases and architectures for Apache Kafka and Event Streaming in Pharma and Life Sciences. The technical example explores drug development and discovery with real time data processing, machine learning, workflow orchestration and image / video processing.

Use Cases in Pharmaceuticals and Life Sciences for Event Streaming and Apache Kafka

The following shows some of the use cases I have seen in the field in pharma and life sciences:

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Image Recognition for Product and Shelf Monitoring and Analysis

With the e-commerce boom, entrepreneurs have learned that conventional strategies of visual merchandising or sales promotions won’t be able to sustain profits in the cutthroat CPG industry. Many retailers are already implementing AI and image recognition to deliver the next level of customer experience, bringing the dawn of a new era for the retail industry. According to Gartner, by 2020, 85% of customer interactions in the retail industry will be managed by AI. Product discovery, product recommendations, and trend analysis are some areas for the implementation of computer vision and image recognition. This article elaborates on how image recognition can be implemented by retail and CPG companies.

1. Auditing Product Placement

Customers are making key buying decisions at store shelves and companies have to use technology to stay ahead of the fierce competition or face extinction. Gathering key consumer information helps companies understand their needs better. Shelf recognition using computer vision digitizes store checks and is important in gathering key consumer information through AI.