Over the last decade, TensorFlow and PyTorch have been the two dominant deep-learning frameworks. Initially released by Google in 2015, TensorFlow had a first-mover advantage but suffered from usability and breaking changes across versions. PyTorch, developed by Facebook (Meta) in 2016, took a different approach, focusing on a more Pythonic and intuitive experience.

Now, the question remains: Is TensorFlow still relevant, or has PyTorch completely taken over? While PyTorch has grown significantly, TensorFlow still holds ground in some areas. This post takes a practical and career-oriented look at where both frameworks stand today, their strengths, weaknesses, and whether TensorFlow is worth learning today.

TensorFlow vs. PyTorch: A Quick Comparison

Feature	TensorFlow (TF)	PyTorch
Ease of Use	More complex, especially in TF 1.x; TF 2.x improved significantly but still not as intuitive as PyTorch	More Pythonic and user-friendly, especially for dynamic graphs
Adoption	Still widely used, especially in enterprise and production settings	The most popular choice in research and increasingly in production
Learning Curve	Steep, especially for beginners; TF 2.x improved this with eager execution	Easier for newcomers due to its dynamic computation graph
Performance	Optimized for large-scale production deployments, good for edge devices (TensorFlow Lite)	Great for research and experimentation; production capabilities improving
Community Support	Large community, but fragmented between TF 1.x and TF 2.x users	Rapidly growing community with strong academic and industry backing
Industry Use	Used in many large-scale enterprise systems and cloud platforms	Increasingly becoming the industry standard, especially in academia and research
Model Deployment	Strong deployment options (TensorFlow Serving, TensorFlow Lite, TensorFlow.js)	TorchServe and ONNX support deployment, but TensorFlow has a more mature ecosystem

Why TensorFlow is Still Used

Despite PyTorch's rising dominance, TensorFlow has not disappeared. It remains relevant for a few key reasons:

1. Beginner Courses Still Use TensorFlow

Many of the best introductory machine learning courses still teach TensorFlow and Keras. A great example is Andrew Ng's Machine Learning Specialization, which introduces students to machine learning using TensorFlow.

However, this shouldn't deter you from taking such courses. The primary goal of learning ML isn't to master a specific framework but to understand core ML concepts, models, optimization, regularization, etc.

Most beginner courses teach only the basics of TensorFlow, so even if the industry shifts to PyTorch, learning TensorFlow in a beginner course won't hurt you. Frameworks change, but fundamental concepts remain the same.

2. Legacy Code and Enterprise Systems

TensorFlow has been around longer than PyTorch, meaning many legacy models and research papers were written using TensorFlow. Many companies still have production ML models running on TensorFlow, and updating these to PyTorch would require significant effort.

Knowing TensorFlow, even at a basic level, helps when:

Reading old research papers that provide TensorFlow implementations.
Maintaining or migrating legacy ML projects.
Understanding large-scale deployment systems built on TensorFlow Serving.

Even if PyTorch becomes the industry standard, TensorFlow knowledge will still be useful in enterprise environments.

3. Google's Continued Support & Ecosystem

Google continues to support TensorFlow and integrate it across its cloud platforms and AI-powered applications. TensorFlow offers:

TensorFlow Lite: Optimized for mobile and edge devices.
TensorFlow.js: Enables ML model execution in web browsers.
TensorFlow Serving: A mature, scalable way to deploy models in production.

However, Google has also invested heavily in JAX, a newer framework for high-performance numerical computing. Some of Google's latest research and internal AI projects are shifting towards JAX, raising concerns about TensorFlow's long-term future. While TensorFlow remains widely used, the increasing focus on JAX could mean fewer cutting-edge advancements for TensorFlow in the coming years.

Why PyTorch is Taking Over

Despite TensorFlow's relevance in specific areas, PyTorch is becoming the dominant ML framework in research, academia, and many production environments.

1. Better Design from the Start

TensorFlow suffered from first-mover issues, including:

A static computation graph in TF 1.x makes debugging harder.
Frequent breaking changes between versions (especially from TF 1.x to TF 2.x).
A steep learning curve due to its complex API.

PyTorch was built later, meaning it could learn from TensorFlow's mistakes. From the start, PyTorch had:

A dynamic computation graph, making debugging and experimentation easier.
A more Pythonic approach, making it intuitive for developers.
Better usability in research, which led to widespread academic adoption.

2. Industry Adoption & Community Growth

PyTorch's ease of use led to massive adoption in academia, and now companies are following suit.

Major AI research labs and universities almost exclusively use PyTorch.
Big tech companies (Meta, Microsoft, Tesla, etc.) are adopting PyTorch for their AI models.
AWS chose PyTorch as its cloud ecosystem's default deep learning framework.

With this rapid growth, PyTorch is becoming the default framework for new ML projects.

3. Growing Production Capabilities

One of TensorFlow's biggest advantages was its superior production tools. However, PyTorch is catching up.

TorchServe is making it easier to deploy PyTorch models.
ONNX (Open Neural Network Exchange) allows for cross-framework compatibility.
Better cloud integration makes PyTorch more attractive for enterprise use.

While TensorFlow still has a more mature production ecosystem, PyTorch is closing the gap fast.

Potential Disadvantages of TensorFlow in Today

While TensorFlow remains useful, it has some notable downsides:

Usability Issues: Even with TF 2.x improvements, PyTorch remains the more intuitive and beginner-friendly option.
Fragmentation: Many users are still stuck between TF 1.x and TF 2.x, leading to compatibility challenges.
Industry Trends: Research, academia, and even some industries increasingly favour PyTorch.
Google’s Shift to JAX: While TensorFlow remains supported, Google is heavily investing in JAX for high-performance computing and deep learning research. This could lead to reduced long-term innovation in TensorFlow.

Should You Still Learn TensorFlow?

If you're starting in machine learning, PyTorch is the better choice. It's becoming the industry standard, is easier to learn, and has more research adoption.

However, learning TensorFlow isn't a waste of time. If a course teaches TensorFlow, take it anyway; the framework isn't as important as the core ML concepts.

If your goal is a long-term ML career, you'll likely use multiple frameworks over time. Machine learning frameworks will evolve, but core concepts like optimization, architectures, and model evaluation remain unchanged.

Conclusion

In 2025, TensorFlow will still be relevant, but PyTorch will take over. While TensorFlow remains important in enterprise systems, cloud platforms, and beginner courses, PyTorch has become the framework of choice for research and new ML projects. However, Google’s shift towards JAX introduces further uncertainty for TensorFlow’s future.

That said, your ML career won't be defined by which framework you know. The best engineers adapt, learn multiple tools, and focus on core ML concepts. Instead of focusing on deep learning framework choices, master deep learning fundamentals. Frameworks will come and go, but knowledge lasts forever.

Is TensorFlow Still Relevant in 2025?