What is TensorFlow and What are its Real World Applications

TensorFlow is an open-source library created by the Google Brain team to build enterprise-grade machine learning algorithms. TensorFlow bundles together a host of machine learning models and algorithms and with the use of common programming frameworks, makes them useful. TensorFlow uses Python and JavaScript to build user friendly APIs for connecting with apps, and uses core C++ to execute the app functionalities.

While it is still early days for machine learning technology, it continues to evolve rapidly, bringing us into a new world of advanced algorithmic thinking, plus deep learning too. Deep learning makes use of algorithms that people usually call Neural Networks , and yes the title is pretty telling. They take inspiration from our biological nervous systems, sort of guided by the brain, in order to process information in a more natural style. In practice, deep learning algorithms help computing devices pick up on virtually every single bit of data, figure out what it means, and then learn repeatable patterns over time. And, honestly, these same advancements are also supporting organizations to boost customer engagement, automate routine workflows, and strengthen decision-making. All of this is happening through salesforce consulting services that lean on AI-driven insights and smart automation, which feels almost like a quiet upgrade happening in the background.

TensorFlow is a tool to develop deep learning models.. It is an open-source AI library that uses data flow graphs to build learning models. With TensorFlow, programmers can build large-scale, multi-layered neural networks. TensorFlow is primarily used to perceive, understand, classify data and create predictive models.

Main Use Cases of TensorFlow

While TensorFlow can be used for many applications, here are 5 commonly used applications in the world of artificial intelligence.

Voice/Sound Recognition

One of the most popular use cases of TensorFlow is audio signal based applications. When fed appropriate data, neural networks can perceive and understand audio signals. These can be:

• Voice recognition — primarily used in Internet of Things (IoT) applications, Automotive applications (Voice command based actions), Security (Authentication)
• Voice search — Commonly used in Telecom and by mobile phone manufacturers
• Sentiment Analysis — used in CRM applications
• Flaw Detection (noise analysis) —Automotive and Aviation applications

The world is familiar with the common use case of voice-search and voice-activated assistants. This use case has been widely popularised by smartphone manufacturers and Mobile OS developers such as Apple’s Siri, Google Assistant and Microsoft Cortana.

Understanding and analyzing language is another widely used use case for Voice Recognition. Speech-to-text applications are used to extract and understand sound bites in larger audio files, and convert it into text.

CRM is another area were voice/sound based applications can be implemented to deliver a better and smarter customer service experience. Imagine a scenario where TensorFlow algorithms fill in for customer service reps, and guide customers to the right set of information much faster than an agent.

Text Based Applications

This is another commonly used application of TensorFlow. Text-based applications, for instance sentiment analysis, can be used in CRM apps and social media for improving the customer or prospect experience. companies that use salesforce often integrate these AI-powered solutions for better customer engagement and analytics. Threat detection, used in social media and government applications, and fraud detection, used by insurance and finance companies, are some common examples.

Language Detection is another popular use case of TensorFlow for text based applications.

We are quite familiar with Google Translate. More than 130 languages can be translated into each other using this service. An AI powered version of a translate engine can be used in common real world situations like translating heath diagnosis technical terminology or legal jargon in contracts into plain language.

Text Summarization

Google also came up with sequence-to-sequence learning, a technique for shorter text summarization. This can then be used to build headlines for news and articles. Another use case for TensorFlow popularised by Google is SmartReply. It automatically generates e-mail responses based on text recognition and contextual understanding.

Image Recognition

This use case is primarily used by Social Media and Smartphone Manufacturers. Facial Recognition, Image Search, Motion Detection, Computer Vision and Image Clustering are nowadays being deployed in Warehousing, Healthcare, Automotive, and Aviation Industries. Google Lens is another example where Image Recognition is being used to understand the content and context to help identify people and objects within images.

TensorFlow object recognition algorithms have the ability to identify and classify random objects within larger images. This has found use in engineering modelling applications such as creating 3D spaces from 2D images. Facebook’s Deep Face is another example of photo tagging using image recognition technology. Deep learning technology can identify an object in an image never seen before by analyzing thousands of images with similar objects. Today, these advancements are also being enhanced through generative ai prompts, which help AI systems create, interpret, and refine visual outputs more accurately across image recognition and computer vision applications.

Healthcare Industry is also at the cusp of using Image Recognition for faster diagnosis.  TensorFlow algorithms can process information and recognize patterns much faster than the human eye to spot illnesses and detect health problems faster than ever.

Time Series

TensorFlow Time Series algorithms is another method used today to establish patterns and forecasting of time series data. Meaningful statistics can be derived by these algos along with recommended actions. TensorFlow Time Series algorithms allow forecasting of generic time periods apart from generating alternative versions of the predicted time series.

A popular use case for time series algorithms is in recommendations, you know , the kind where history kinda repeats itself. Time Series recommendations has been used a lot across major orgs like Netflix, Google, and Amazon, where they look at and compare the activity of millions of people to figure out what someone might want to watch or buy. With each interaction, and recorded action, these recs get sharper over time. Like, they might suggest a show or video that your family members, or even friends, tend to enjoy, or maybe recommend gifts you could actually like. In a similar way, a salesforce implementation partner can use time series data plus predictive analytics to help companies craft more personalized customer experiences, boost engagement, and deliver more effective recommendations across sales and marketing channels.

Finance, Insurance, Government, Security and Threat detection, Predictive Analysis, Resource Planning and forecasting are some of the other use case scenarios of TensorFlow Time Series algorithms.

Video Detection

TensorFlow deep learning algorithms can also be used on video data. This is used in Motion Detection in Automotive and Aviation, Role based Gaming, Security and Threat Detection. Today, universities are doing deep research on Video Classification at a large scale to perceive, analyze, understand, classify video data. NASA is using TensorFlow algorithms to build a system for orbit classification and object clustering of asteroids. Consequently, they will be able to classify and predict near earth objects.

TensorFlow is an open-source framework, allowing developers the freedom to work on innovative and disruptive use cases, which will contribute further to Machine Learning technology.

Amongst many things, TensorFlow’s popularity is primarily due to the computational graph concept, automatic differentiation, and the adaptability of its python API structure. This makes TensorFlow more accessible to developers to solve real problems. Here are some advantages of TensorFlow.

1. Scalable

The TensorFlow library is well defined and structured. This means it works just as efficiently on a mobile device as on a powerful computer.

2. Open Source

The TensorFlow library is available free of cost. Anyone, anywhere can work on it and use it to solve problems.

3. Graphs

Tensorflow has a very powerful, inbuilt data visualization capability. This makes it easier for developers to work on neural networks.

4. Debugging

Tensorboard, which is a part of TensorFlow, allows easy debugging of code blocks. This reduces the need for combing through the whole code.

5. Parallelism

TensorFlow uses Central Processing Unit (CPU) and Graphics Processing Unit (GPU) for its functioning. Developers can use the architecture freely based on the problem they are trying to solve. A system uses GPU by default, which is why  TensorFlow is sometimes referred to as a hardware acceleration library because it reduces memory usage.

6. Compatible

TensorFlow is compatible with popular programming languages like Python, C++, JavaScript, etc. This allows developers the freedom to work in an environment they are most comfortable with.

7. Architectural Support

The TensorFlow architecture uses Tensor Processing Unit (TPU). This makes computation faster than what one would get when using CPU and GPU. TPU models can be easily deployed on the cloud and work faster than CPU and GPU.

8. Library management

With the Google backing, TensorFlow is updated regularly with enhanced capability and flexibility with every release.

AI is already an intrinsic part of Salesforce, the world’s leading CRM platform. As a Gold Salesforce Partner, Girikon is the preferred choice for many Salesforce customers across the globe. To know more about how AI can work for your business, contact us today.

About Author

Indranil Chakraborty

Indranil is a technology enthusiast with over 25 years of experience in project management, operations, technology and business development. Indranil has led project teams in egovernance, business process re-engineering, product development and worked with Government and Corporate customers. Indranil truly believes in the power of technology to drive productivity and growth for teams and businesses.