TensorFlow (developed by Google) and PyTorch are the two main deep learning libraries for Python, with other libraries being less popular.
In our research and outreach, we gathered economic information about businesses that rely on AI and benefit from open-source technologies, such as TensorFlow, PyTorch, and other related tools.
We also spoke to experienced ML researchers to gain insights into the role of TensorFlow in their daily work, such as how they use it to create powerful machine learning models and how it has become an integral part of their workflow.
TensorFlow and the Life of ML Engineers
TensorFlow has revolutionized the way machine learning engineers work by providing them with powerful tools to efficiently solve a wide range of tasks. Valentine Shkulov, a Staff ML Engineer at Meson Capital, commented, "It has made the construction and optimization of multilayer neural networks a routine process, with various types of layers and parameters. This is currently essential for efficiently solving a vast array of tasks, such as Object Detection, Image Classification, Speech Recognition, and Language Modeling."
He added, "All modern state-of-the-art algorithms are based on neural networks and are published with only the architecture of different layers and parameters. These can be found as ready-to-use components in TensorFlow, such as convolutional, recurrent, attention, and regular dense layers with various activation functions and sizes."
It still is an essential tool for developing high end and simultaneously high-performing solutions for various business needs and scientific tasks. "Expertise in libraries like TensorFlow is a key feature for ML engineers to stay current with the tech stack," Shkulov suggests. "It's not only about gaining more flexibility with a wider range of possible approaches and solutions based on neural networks, but it also becomes a unique technology for several tasks in the fields of Computer Vision and Natural Language Processing, such as the famous Diffusion Models and GPT.”
TensorFlow is powerful
Let's take a look at the working pipeline. Argo Saakyan, a Computer Vision Researcher, explains that when you have a task, such as computer vision or image classification and you have your data, you need to use a deep learning model. There are several ways to go about this. You can write a custom classifier in TensorFlow, or you can take advantage of great architectures that have been pre-trained on huge datasets such as ImageNet and use them.
"It's done with a couple of lines of code with TensorFlow, and that's really powerful. Next, you need to optimize your model for deployment, and that's built into TensorFlow too!" Saakyan remarked.
Saakyan also mentioned that you can use TF-TRT (TensorFlow-TensorRT) to make your model run 2-5 times faster on Nvidia GPUs, calling it "insane" but not the end of the story. You can use TensorFlow Serve to run optimized models and TensorFlow Lite to run your models on mobile devices.
Job Prospects for AI Careers with TensorFlow Skills
Investing time and effort in building ML skills with TensorFlow can still be highly rewarding. It has been observed that those who possess a comprehensive understanding of TensorFlow are more likely to secure employment quickly.
Data from ZipRecruiter shows that job roles requiring TensorFlow proficiency can offer salaries of up to $193,500 per year. The average salary for a TensorFlow Developer in the United States ranges between $93,500 and $143,500, with the highest earners making $184,000 annually.
Having programming and math skills makes it relatively easy to learn TensorFlow. However, even experienced programmers may find certain concepts intimidating. With the right training resources, though, you can gain the skills needed for job roles that require proficiency in TensorFlow or PyTorch.
TensorFlow: Critics' Viewpoint
Lian Huang wrote the following statement in response to the question "What is the Future of TensorFlow?" on Quora:
Huang argues that TensorFlow, due to its design logic of static graphs, is no longer a viable option for ML/AI language models. PyTorch and Dynet, with their dynamic graphs, are much easier to use and debug. Despite Google's significant investment in TensorFlow, the mistake of its design is so fundamental that it cannot be redone.
Huang predicts that in the future, few people will still use TensorFlow, making this mistake one of the most costly Google has ever made in AI.
Lian Huang's views on the future of TensorFlow are also credible because of his extensive academic portfolio and experience, including a Ph.D. from the University of Pennsylvania, a professorship in computer science, research scientist positions at Google and IBM, awards for his work at ACLs and EMNLPs, recognition as an award-winning teacher of theoretical computer science, and status as a best-selling author in China.
As an expert in ML/AI language models, his views on the future of TensorFlow are worth considering.
TensorFlow is popular for its user-friendly interface and powerful numerical computation capabilities. We believe a vibrant community of data scientists ensures its longevity, allowing users to explore, create, refine, and deploy deep learning models.