Hire TensorFlow Developers

TensorFlow Developer Development Technologies

Python

Python is the main programming language used to build models and applications on TensorFlow. The TensorFlow program provides access to workflows that can be used to develop and train models with the help of Python. These models are then deployed on the server, be it on the cloud or an on-premises one. 

Even though the functional programs are written in Python, the application built with TensorFlow can run on any device irrespective of the language used. This is possible with the tf. Data input system that allows building complex input pipelines from existing code scripts. 

C++

The TensorFlow developers can use the C++ API library to further augment the development aspects of their program. At present, TensorFlow supports only the C++ Session Interface, and the C API. TensorFlow developers can use any of these systems to execute the dataflow graphs. 

C++ makes TensorFlow fast in terms of computing matrix multiplication. These functions are implemented in C++, but they can also be accessed and controlled by other programming languages, including Python. 

CUDA

CUDA stands for Compute Unified Device Architecture, and it corresponds to a parallel computing and programming system built by NVIDIA. CUDA helps TensorFlow developers with executing the real-world TensorFlow training data, and it requires the processing power of GPU. 

As a result, CUDA provides higher processing power to the developers and increases the speed of compute-intensive applications. Generally, these applications take time to process, but CUDA lends GPU’s power to these processes to make them more efficient. 

TensorBoard

TensorBoard is another vital element you need to consider to hire TensorFlow developers. Also known as TensorFlow’s visualization toolkit, TensorBoard helps provide visualizations and tooling required for experimentation with machine learning applications. The developers can track and view the metrics, model graphs, histograms, images, read audio data, etc. 

The motive of using TensorBoard is to track metrics like loss and accuracy in relation to machine learning experiments. As an extension to this, the developers can use the results provided by TensorBoard to test and debug the experimentation models, optimizing them for best performance. 

TensorFlow Probability

TensorFlow Probability or TFP is a Python library helping combine probabilistic models and deep learning systems on modern hardware. TFP is specifically meant for data scientists, statisticians, and machine learning researchers. These professionals use TFP built on Python to understand the data and make predictions. ‍

TensorFlow Federated

TensorFlow Federated (TFF) is the framework TensorFlow developers use for machine learning computations based on decentralized data. This framework is built to help the beneficiaries with open research and its experimentations via federated learning. 

Federated learning refers to an exercise whereby the shared global model is implemented and trained amidst different clients to localize their training data. Federated learning is a machine learning approach, and it is used on TensorFlow to connect it with machine learning concepts. 

TensorFlow has brought the possibilities created by machine learning to general use. Today, a wide range of organizations are using machine learning-based applications for different purposes. Some of the applications of TensorFlow include building systems for voice/sound recognition, image recognition, video detection, live location tracker, self-driving cars, chatbots, etc. 

Being a complex program, you need to prepare well to hire TensorFlow developers. As the implementation of the program is complex, you need developers who are good at understanding the entire program and developing applications with the same. 

With the industry dynamics, the workplace settings are changing, and companies are finding it hard to sourcing good talent and sustain operations, especially when the teams are segregated. Instead of dealing with the issues that arise due to these changes, switch to Skuad. 

We have industry-wide expertise in helping companies source the best talent and manage the teams while ensuring complete transparency. Our hiring practices are driven by a motive to help companies work with the most qualified professionals who are also good at their respective fields. 

TensorFlow Developer Development Industries

Tourism and Real Estate

Airbnb is the biggest example in this industry for a company that is using TensorFlow to improve the company processes. Specifically, Airbnb uses TensorFlow to build a system that classifies images and detects objects. This is done to improve the customer experience. 

Healthcare

GE Healthcare sets an example of the advanced use cases of TensorFlow. This healthcare company uses TensorFlow to train a neural network system for identifying the brain’s anatomy during an MRI exam. As a result of its capabilities, TensorFlow helps improve diagnostic accuracy and enhance speed. 

Computer and Software

We can provide several examples of companies using TensorFlow to create bespoke systems and programs for enhancing their operations or systems. Intel uses it to improve the inference performance, and Lenovo is leveraging its abilities to accelerate AI-based training aimed at improving high-performance computing. 

Finance

One of the finest examples of online financial platforms, PayPal, is using TensorFlow’s deep transfer learning to generate models that predict and detect fraud patterns. They are using this technology to improve the safety of the platform and provide a better user experience. 

The number of industries and the ways in which they are using TensorFlow are numerous. Coming with benefits like scalability, efficient debugging, and amazing community support, TensorFlow is one of the best technologies to use for a company's growth. 

However, you need to hire TensorFlow developers who can work on all these in-built and connected applications to help you build the perfect product. TensorFlow has great futuristic applications, which attracts a great deal of attention and developers. 

Skuad specializes in hiring technical professionals from all fields. We understand your requirements and then create the required recruitment processes. Associating with Skuad means that you can hire TensorFlow developers at cost-effective rates and with a flexible system. 

Hard Skills Requirements for TensorFlow Developer

• Knowledge of machine learning and deep learning along with their algorithms and their work procedures. 
• Experience of working with Python and C++. 
• Understanding of neural networks and their impact.
• Good at analyzing data and businesses. 
• Proficient in TensorFlow and its applications. 
• Understanding the workings of a cloud environment. 
• Ability to articulate the best data flow structures for implementing machine learning pipelines. 
• Work to identify the requirements of the project and the motive behind using TensorFlow to create the project. 
• Demonstrable experience in breaking down complex project requirements into small understandable parts for easy development. 
• Has worked with cloud platforms like AWS or Azure. 
• Can write, train, implement and debug the coding scripts and modules based on machine learning. 
• Create internal tools and guides for accessing the machine learning models to help the team visualize results and understand model performance. 
• Can write easy to follow code. 
• Preferably having experience with Kubernetes, Docler, data engineering, and Machine Learning Ops. 
• Understands the future impact of Artificial Intelligence and brings that understanding into work while developing projects. 
• Has the ability to create prototypes and proof of concept quickly while evaluating the impact of all the technologies and frameworks that must be used. 
• Familiarity with CI/CD tools along with standard development practices. 

Soft Skills Required for TensorFlow Developers

• Shows the enthusiasm to learn new things and work on upskilling. 
• Participates in team discussions and brainstorming sessions to bring new ideas to the table. 
• Collaborate with other teams and developers.
• Can adhere to deadlines and complete the work on time. 
• Shows the ability to handle a development team of junior developers, helping them get better at their skills. 
• Demonstrates impressive interpersonal and communication skills.
• Can work independently in a remote environment.