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Business Applications of TensorFlow

Business applications of TensorFlow: a projection chart of a behavior prediction on the table

As a company that delivers ML-based solutions on a regular basis, SOLVVE has spoken about artificial intelligence a lot. From its history to trends, to specific application cases within different business domains like eCommerce, Education, eSports, and so on. However, we never talked about TensorFlow, one of the core technologies that helps to deliver all these solutions. That is why in this article SOLVVE is going to explain in more detail what TensorFlow is and how it can benefit your business.

What is TensorFlow?

TensorFlow is an open-source ML-library by Google that allows to create and train deep learning models. These models have proved their efficiency and TensorFlow has made it rather easy for engineers to work with them as well as have access to high-quality data. 

To understand how deep learning works, let us take a quick look at image recognition. If you want to teach your algorithm to understand if a certain object is in a picture, at first you have to show it an example, e.g. a picture of a dog, and confirm that this is a dog. Later, your algorithm tries to identify features that help to distinguish dogs from other objects on different images. It starts from something simple. For example, the silhouette.

Then, it goes through the set of pictures, and every time it guesses correctly, you have to confirm the guess, otherwise mark the mistake. With every mistake, the algorithm adjusts its processes, finds new types of information that are specific to dogs, and becomes more and more precise in recognizing dogs.

To identify new features the system goes through layers of data. These layers are presented as nodes in a neural network. An algorithm flows from one node to another in a process called tensor, giving TensorFlow its name.

Besides being free and comparatively easy to use, its core power lies in its data libraries. Before TensorFlow, as Wired has pinpointed, academics and startups did not have easy access to data and had to build their neural networks from scratch. However, with TensorFlow, anyone can both do research and develop solutions for business applications.

What can you do with TensorFlow?

TensorFlow has an impressive list of functions it can perform. However, in short for non-techies, researchers, data scientists, and developers can use the toolset of TensorFlow to work on their individual projects or collaborate to refine and scale existing solutions. From a business application standpoint, TensorFlow has three functions that bear the most value for commercial products:

  • Data analysis for behavior prediction.
  • Object detection, image recognition and processing.
  • Natural language processing.

Let us take a look at specific examples of products and services that already exist thanks to functions in each category. 

Examples of Business Applications of TensorFlow

Data analysis for behavior prediction

The core principle is to make rather accurate forecasts about different types of behavior in a broad sense of the word. 

For example, SOLVVE can help its clients to forecast brand development based on previous information about the market. By taking a look at the historical data across a specific business domain, type of company, their products, target audience, and other characteristics, it is possible to accurately predict with a high level of accuracy how the company will perform on the chosen market in a short- and long-term perspective. 

This technology can also be applied to human behavior. SOLVVE had developed a system that works in conjunction with security surveillance and automates the shoplifting prevention process. Similar to the case above, the algorithm utilizes data about typical human behavior during shopping. Once it sees patterns that differ from a set of the default behavior, it can send automatic alerts to the assigned crime prevention unit. 

Object detection, image recognition and processing

Image recognition and object detection are sometimes used as synonyms. Of course, both belong to computer vision techniques. However, they perform different types of tasks and thus serve different business purposes. You can learn about the theoretical difference in our article here

Going back to practical applications, SOLVVE has used TensorFlow to develop an algorithm to enhance the accuracy of blood analysis with a combination of instance segmentation (the one we previously used for text recognition and score extraction) and object detection. It helps to identify certain types of blood cells by comparing test samples to the constantly updating image dataset. 

One more example of image processing in action Teleplay, an innovative ML-enhanced application for children. It allows them to place their toys in virtual reality without the use of chromakey. To do that SOLVVE team developed a feature that could distinguish toys and hands from the background through a device camera and remove unnecessary elements to deliver “magic” experience where kids can create their worlds and cartoon settings at home.

Natural language processing

Google widely uses TensorFlow in conjunction with functions that have to do with how people speak or write. 

For example, when you type something in a search bar, you get your suggestions based on the frequency of similar searches. Moreover, your location, previous search, interests, and some other actors come into play to give you the most relevant suggestions.

More sophisticated examples include real-time subtitling on YouTube. A speech recognition algorithm automatically generates text on the go. Although the quality may vary depending on the language, peculiarities of pronunciation, background noises, and other factors, it is still quite impressive considering that the platform hosts videos about the endless list of topics in so many languages.

Google assistant combines both of these properties. You can freely talk to it in your language and it will provide you search results in a variety of formats. Moreover, if you watch closely what happens on the screen of your device while you talk to it, you will also see how Google uses a speech-to-text function to type in and display on your screen what you have just said. All of these have to do with natural language processing.

To conclude

As you see, TensorFlow is one of the key tools in machine learning for business applications. It is widely popular among engineers and lies at the core of many popular applications as well as of some very niece custom-developed solutions. It can be used to enhance business across different domains. Healthcare, Education, eCommerce, eSports, and so forth rely on TensorFlow to deliver better and more precise services.
SOLVVE offers its strong expertise in artificial intelligence and a team of experienced machine learning experts to help you implement your business idea on the market. Thus, if you have any questions or ideas about using TensorFlow for your projects or applications, do not hesitate to contact us. Let us make it happen!