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A Brief Insight Into TensorFlow Development and Its Features

   John Doe      June 21st, 2021

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TensorFlow is a popular term in the field of Artificial Intelligence (AI) and Machine Learning (ML) application development. It is an open-source, symbolic math library created by Google and often used to implement Machine Learning and deep learning systems. The platform generally uses data flow graphs to create models. With the help of this platform, developers can build large-scale neural networks with several layers. The applications developed using TensorFlow help machines to recognize images in an insightful and significant manner. 

One of the most interesting things about this platform is that the applications developed using TensorFlow can run successfully on various platforms or things such as a cluster in the cloud, a local machine, CPUs, GPUs, iOS devices, Android devices, etc. Another interesting thing to know about TensorFlow is that presently Google’s TensorFlow is one of the most popular deep learning libraries in the world. Google makes use of ML in all of its products to enhance the search engine, image captioning, recommendations, or translation. As a result, users can experience a quick and more refined search result. 

An ideal solution for numerical computation and deep learning, the platform helps users to carry out a number of tasks by feeding huge volumes of data. Some of the key TensorFlow development services that are widely used by different types of businesses include:

• Image/Voice Recognition and Processing
• Image Classification
• OCR and ICR
• Chatbot Services
• Complex Numerical Computations
• Outcome Predictions

Key Features of TensorFlow

TensorFlow is one of the best platforms that allow users to collect datasets, train systems through models, and provide results based on them. Below mentioned are some of the key features of TensorFlow that makes it an ideal framework for developing various ML and AI-based applications:

• Open-Source: TensorFlow is an open-source library that facilitates faster and easier calculations in ML. It also simplifies the switching process of algorithms from one TensorFlow tool to another. Besides, with the help of the Python programming language, it provides a front-end API for the development of numerous machines and deep learning algorithms. 
• Responsive Design: The platform allows users to monitor and envisage each and every part of the graph easily. 
• Compatible with Various Platforms: The applications built using TensorFlow can run seamlessly on various platforms including iOS, Android, Cloud, and architectures like GPUs and CPUs. 
• Quick Debugging: TensorFlow allows users to reflect each operation individually based on its evaluation. The platform offers computational graphing techniques that support an easy to carry out model. 
• Scalability: With the help of training by utilizing the same models and various data sets, TensorFlow provides the ability to make predictions related to products, stocks, tools, etc. It also facilitates synchronous and asynchronous learning techniques and data ingestion.
• Abstraction: TensorFlow offers a distinct level of abstraction by reducing the code length, thereby decreasing the development time. The users are just required to focus on logic instead of paying attention to the apt way of providing input to functions. The user can select the most appropriate model based on the system’s requirements.
• Feature Columns: TensorFlow facilitates feature columns that act as a bridge between the raw data and estimators to train the model and add agility to it for swift developmental insights.
• Parallel Neural Network Training: The platform lets users train multiple neural networks and multiple GPUs at the same time, which makes the models quite efficient, especially on large-scale systems. 
• Flexibility: TensorFlow provides the flexibility to resolve various complex topologies with the help of Keras API and data input pipelines. Keras offers simple prototyping, thus best suitable for creating object-oriented neural networks. The platform also allows users to build and manipulate the system to form various types of real-time models.
• Huge Community Support: Since TensorFlow is developed by Google, it gets huge community support from professional software engineers and experts throughout the world who have excellent know-how of this platform and related activities. Developers or beginners can get the required help from these professionals as and when required to accomplish their development projects quickly and efficiently. 

So, these are some of the key features of TensorFlow that make it one of the most preferred platforms by developers for building and deploying ML-powered applications.  

Why Hire Developers for TensorFlow Development Projects?

When planning to utilize TensorFlow for an application development project, it is advisable to hire TensorFlow developers who have deep knowledge, experience, and all the required skills to complete a project successfully within the specified time period. 

Key Skills of TensorFlow developers

So, the skills that one must look for when hiring TensorFlow developers include:

• Detailed knowledge of Machine Learning (ML) and deep learning
• Knowledge of software development life cycle
• Know-how of Agile methodology and CI/CD concepts
• Proficiency in data and business analytics
• Thorough understanding of advancements in neural networks
• Excellent knowledge of learning algorithms and their working
• Good knowledge of various programming languages such as Python, C++, Java, etc.

Hiring professional developers with enough experience in this field can help businesses to get customized solutions to best meet their specific project requirements. 

Conclusion:

With a gamut of features and functionalities, TensorFlow has become one of the best open-source libraries to build and deploy various production models on different platforms. Since it offers a simple and flexible model building experience, it is suitable for both experts and beginners.