Framework Selection is no longer a religious war between fans; it is a strategic business decision.

The landscape has settled into a functional duopoly for Deep Learning (PyTorch vs. TensorFlow) and a clear standard for Classical Machine Learning (Scikit-Learn). Choosing the wrong one can lead to "Technical Debt"—where your researchers write code that your engineers cannot deploy.

Here is the detailed breakdown of the ecosystem, the "Research vs. Production" divide, and the decision matrix, followed by the downloadable Word file.

1. The Big Two: PyTorch vs. TensorFlow

A. PyTorch (The Research King)

• Philosophy: "Imperative" (Eager Execution). You write Python, and it runs line-by-line. If there is a bug on line 5, it crashes on line 5. It feels exactly like NumPy.
• Dominance: It powers 90%+ of modern research papers, Generative AI, and LLMs (Hugging Face is built primarily on PyTorch).
• Best For: R&D, GenAI, rapid prototyping, and complex dynamic architectures.

B. TensorFlow / Keras (The Production Workhorse)

• Philosophy: "Declarative" (Graph-Based). Historically, you defined a graph, then compiled it. TensorFlow 2.0 became more like PyTorch, but its legacy is deep.
• Ecosystem: Its strength is TFX (TensorFlow Extended)—a massive suite of tools for data validation, model serving, and mobile deployment (TFLite).
• Best For: Mobile apps (Android), embedded devices, and massive enterprise pipelines where strict governance is needed.

2. The Specialized Frameworks

A. JAX (The Speedster)

• What is it? Developed by Google. It is essentially NumPy on steroids (running on TPU/GPU) with automatic differentiation.
• Superpower: XLA (Accelerated Linear Algebra). It compiles your Python code into super-optimized machine code.
• Best For: High-performance scientific computing and massive-scale model training (used by DeepMind).

B. Scikit-Learn (The Foundation)

• What is it? The standard for "Classical" ML (Regression, Clustering, Support Vector Machines).
• Rule of Thumb: If you have tabular data (Excel sheets) and < 1 million rows, use Scikit-Learn, not Deep Learning.
• Best For: Forecasting, Customer Segmentation, and Fraud Detection on structured data.

3. The "Swiss Army Knife": ONNX

The fear of "Lock-In" is solved by ONNX (Open Neural Network Exchange).

• The Workflow: You can train your model in PyTorch (because it is easy to write) and then export it to ONNX.
• The Deployment: You can then run that ONNX file using C++, C#, or Java on a server that doesn't even have Python installed.
• Strategy: "Write in PyTorch, Deploy in ONNX."

4. Decision Matrix: Which one do I choose?

Scenario	Recommendation	Why?
Generative AI / LLMs	PyTorch	The entire GenAI ecosystem (Hugging Face, LLaMA) is native to PyTorch.
Mobile App (iOS/Android)	TensorFlow (TFLite)	Mature, battle-tested tools for shrinking models to run on phones.
Tabular Data (Spreadsheets)	Scikit-Learn / XGBoost	Deep learning is overkill. Gradient Boosting (XGBoost) usually wins Kaggle competitions here.
Massive Math / Physics	JAX	Unbeatable performance on TPUs for pure mathematical simulation.
Newbie / Student	Keras (Core)	Keras 3.0 is now "backend agnostic"—you learn Keras once, and it can run on top of PyTorch OR TensorFlow.