The Future of TensorFlow from the Perspectives of Its Users

TensorFlow (developed by Google) and PyTorch are the two main deep learning libraries for Python, while 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, Kubernetes, 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.

Machine Learning and AI are hot topics in almost every industry today. According to the report from the World Economic Forum, ¹ the growth of ML and AI will not lead massive unemployment but create more jobs than it automates.

This research estimates that AI will displace 85 million jobs, but also predicts that 97 million new jobs will be created across 26 countries by 2025.

As AI continues to revolutionize the nature of work, it presents tremendous opportunities for businesses and individuals to be more creative, strategic, and entrepreneurial.

TensorFlow is powerful for numerical computation, advanced machine learning, deep learning, and analytical workloads, and its parent company Google's reputation makes it a popular choice for complex AI tasks among big names like Airbnb, Twitter, DeepMind, and Intel. ²

• Google uses TensorFlow to improve the information retrieval capabilities at a much larger scale and powers its ML and AI implementations in products like Search Engine, Gmail, Translate, Drive and more.
• Airbnb data science team applies Machine Learning with TensorFlow to classify images and detect objects at scale to improve the user experience.
• PayPal engineering team engages deep transfer learning and generative modeling techniques to identify fraud patterns with TensorFlow.
• NASA employs genetic programming techniques using TensorFlow for orbit classification and object clustering of asteroids and so much more for studying Earth's outer atmosphere.

TensorFlow is widely used across healthcare, social networks, finance, and other industries to implement ML and AI solutions for classification, perception, understanding, prediction, and creation.

TensorFlow and the Life of ML Engineers

TensorFlow has revolutionized the way ML engineers work, providing them with powerful tools to efficiently solve a wide range of tasks. Valentine Shkulov, a Staff ML Engineer at Meson Capital, ³ commented that it made a usual routine the multilayer neural nets constructing and optimization processes with different layers' types and parameters, which currently is necessary to attribute to efficiently solving a huge range of tasks such as Object Detection, Image Classification, Speech Recognition and Language Modelling."

"Due to that all modern state-of-art algorithms are based on neural nets and published with only architecture of different layers and parameters that can be found as a ready-to-use parts in TensorFlow such as Convolutional, Recurrent, Attention and regular Dense layers with different activation functions and size."

To sum up all value brought by TensorFlow to a business, it's the essential tool for developing a high-end and, at the same time, enough performing solution for any type of business needs and scientific tasks. "Expertise in libraries like TensorFlow is a key feature for ML engineer to stay actual with tech stack," Valentine pointed out. "And it's not only about gaining more flexibility with a wider range of possible approaches and solutions based on neural nets, but it becomes a unique technology for several tasks in fields of Computer Vision and Natural Language Processing such as famous Diffusion Models and GPT."

TensorFlow is powerful

Let's take a closer look at the working pipeline. Argo Saakyan, a Computer Vision Researcher, ⁴ 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 in Tensorflow too!

Argo remarked 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 to build ML skills with TensorFlow can still be highly rewarding. It has been observed that those who possess a comprehensive understanding of TensorFlow are likely to secure employment more quickly.

ZipRecruiter data 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 is 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 for job roles that require proficiency in TensorFlow or PyTorch.

TensorFlow: Critics' Viewpoint

Lian Huang ⁵ wrote the following statement in response to the question "What is the Future of TensorFlow?" on Quora.

Huang argues that TensorFlow, because of 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, professor of computer science, research scientist at Google and IBM, awards for his work at ACLs and EMNLPs, award-winning teacher of theoretical computer science, and best-selling author in China.

He is an expert in ML/AI language models, making his views on the future of TensorFlow worth considering.

TL;DR

TensorFlow is popular for its user-friendly interface and powerful numerical computation capabilities. We believe vibrant community of data scientists ensures its longevity, allowing users to explore, create, refine, and deploy deep learning models.

Disclosure: The views expressed in this article are those of the authors and do not reflect the views of TensorFlow, Google, or their partners. This article may contain links to content on third-party sites. By providing such links, kanger.dev does not adopt, guarantee, approve, or endorse the information, views, or products available on such sites.

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References

• AI will lead to long-term job growth by the World Economic Forum. ¹
• Case studies by TensorFlow. ²
• Valentine Shkulov is a Staff ML Engineer at Meson Capital. ³
• Argo Saakyan is a Computer Vision Researcher at Diagnocat. ⁴
• Lian Huang is a CS professor and a former Google scientist. ⁵