TensorFlow vs PyTorch vs JAX: Do You Really Need a Framework for AI/ML?
If you’re exploring AI and machine learning, you’ve probably seen endless debates about TensorFlow, PyTorch, and JAX. But beyond the hype, how do these tools truly fit into building AI systems—and do you always need them?
In my latest article, I break down the AI/ML development stack from top to bottom, clarify when you don’t need frameworks, and show when they become essential. I also compare strengths, use cases, and future trends for TensorFlow, PyTorch, and JAX.
👉 Read the full article here: [TensorFlow vs PyTorch vs JAX: Do You Really Need a Framework for AI/ML?]
What you’ll learn:
The five main layers of the AI/ML ecosystem (APIs, pre-trained models, frameworks, low-level tools, and theory)
How API-based AI (OpenAI, Gemini, Grok) can let you train/tune models without touching frameworks
A direct comparison of TensorFlow, PyTorch, and JAX: when to choose each
How the landscape is evolving: convergence, growth of JAX, the rise of customizable AI APIs
This isn’t just a “which framework is best” article. It’s about picking the right abstraction based on your goals—whether you're using AI or building the next generation of models.
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