ML and DL frameworks

You're building a feature that needs machine learning. Maybe you want to predict customer churn, classify support tickets, or fine-tune a language model for your domain. You search for guidance and encounter a bewildering array of names: TensorFlow, PyTorch, scikit-learn, Keras, XGBoost, NumPy, Pandas. Some tutorials use one, some use three together, and everyone assumes you know which to use when.

This article clarifies the ML/DL framework landscape from a software engineer's perspective. You'll understand what each framework does, how they relate to each other, and most importantly - how to choose the right tool for your specific problem. By the end, you'll know whether you need deep learning at all, and if you do, what trade-offs you're making between different approaches.

Layers, Not Alternatives

The confusion about ML frameworks stems from treating them as competitors when they're actually different layers of a stack. Think of it like web development: you wouldn't compare Express.js to PostgreSQL or React to Docker - they solve different problems at different abstraction levels.

ML frameworks work similarly. At the bottom, you have numerical computation libraries (NumPy). Above that sit data manipulation tools (Pandas). Then come traditional machine learning libraries (scikit-learn, XGBoost) and deep learning frameworks (TensorFlow, PyTorch). Each layer builds on the ones below it.

Most real projects use multiple frameworks together. You might load data with Pandas, preprocess it with NumPy, train a model with scikit-learn for a baseline, then move to PyTorch for deep learning if needed. They're complementary tools, not competing solutions.

NumPy and Pandas: The Foundation Layer

Before discussing ML frameworks, understand that NumPy and Pandas aren't machine learning libraries - they're data manipulation tools that ML libraries depend on.

NumPy provides efficient array operations. Machine learning is fundamentally about manipulating large arrays of numbers (matrices, tensors), and NumPy makes this fast through vectorized operations written in C. Every ML framework either uses NumPy directly or implements similar concepts. When you see arrays, matrices, or tensors mentioned in ML contexts, that's NumPy's conceptual foundation.

Pandas adds structured data manipulation on top of NumPy. It introduces DataFrames - spreadsheet-like structures that make working with real-world datasets practical. You load CSVs, clean missing values, merge datasets, and transform columns with Pandas before feeding data to ML algorithms.

Neither library trains models. Think of them as your data preparation layer: Pandas for loading and structuring data, NumPy for the numerical operations underneath everything. You'll use these regardless of which ML framework you choose later.

Traditional ML: Scikit-learn and XGBoost

Traditional ML algorithms - linear regression, random forests, support vector machines - work on structured, tabular data where you've explicitly defined features. These are your first stop for most ML problems.

Scikit-learn is the standard library for traditional ML in Python. It provides a consistent API across dozens of algorithms: fit a model with model.fit(X, y), make predictions with model.predict(X). This consistency means you can swap algorithms easily while learning which works best for your problem.

The library covers the entire ML workflow: data preprocessing, model selection, hyperparameter tuning, and evaluation metrics. It's designed for structured data problems like predicting house prices, classifying emails as spam, or forecasting sales. If your data fits in a spreadsheet and your features are clear, scikit-learn should be your starting point.

XGBoost (and similar libraries like LightGBM, CatBoost) is specialized for gradient boosted decision trees. These algorithms dominate structured data competitions and production systems for tabular data. XGBoost is faster and often more accurate than scikit-learn's tree-based models, but it's less general-purpose - it does one thing (gradient boosting) exceptionally well.

When your problem involves structured data with clear features, try scikit-learn first. Move to XGBoost when you need better performance on tabular data and have the time to tune hyperparameters. These libraries train in seconds to minutes on typical datasets, making experimentation fast.

Deep Learning: TensorFlow and PyTorch

Deep learning frameworks handle neural networks - models that learn features automatically from raw data. You need these when working with images, text, audio, or when you need to fine-tune pre-trained models like LLMs.

TensorFlow was designed for production deployment at scale. Developed by Google, it emphasizes stability, deployment tools, and running models everywhere from mobile devices to server clusters. TensorFlow's computation graph approach optimizes models aggressively for performance. LiteRT (formerly known as TensorFlow Lite) runs on mobile devices, TensorFlow.js runs in browsers, and TensorFlow Serving handles production model deployment.

The trade-off: TensorFlow historically had a steeper learning curve and less intuitive debugging. Errors happen in the compiled graph, making them harder to trace. Google designed it for large organizations with dedicated ML engineering teams.

PyTorch was designed for research and rapid experimentation. Developed by Meta (Facebook), it uses eager execution - your code runs immediately, like normal Python. This makes debugging intuitive: use print statements, set breakpoints, and inspect tensors at any point. PyTorch feels more "Pythonic" and is easier to learn for developers without deep ML backgrounds.

PyTorch has become the dominant framework in research (most papers publish PyTorch implementations) and increasingly in production. Tools like torch.export and TorchServe (limited maintenance) handle deployment, though TensorFlow still has advantages for mobile and edge deployment.

The practical difference: TensorFlow prioritizes production deployment and stability, PyTorch prioritizes development speed and flexibility. For most developers today, PyTorch's ease of use outweighs TensorFlow's deployment advantages, especially since deployment tools have improved significantly.

Keras: The Controversy and Context

Keras deserves special mention because it confuses many developers. Originally an independent high-level API that could run on TensorFlow, Theano, or CNTK, Keras is now integrated directly into TensorFlow as its official high-level API (tf.keras).

Keras provides a simpler, more intuitive interface for building neural networks compared to TensorFlow's low-level APIs. If you're using TensorFlow, you'll probably use Keras - it's essentially TensorFlow's friendly frontend. Think of it as TensorFlow's answer to PyTorch's ease of use.

The confusion: some resources treat Keras as a separate framework, others as part of TensorFlow. For practical purposes, if you're learning TensorFlow today, you're learning Keras - they're now the same thing.

How They Work Together

Real ML projects combine these tools. Here's a typical workflow for a text classification problem:

Load and explore data with Pandas - read CSVs, handle missing values, explore distributions
Preprocess text with NumPy operations - tokenization often produces arrays of numbers
Try traditional ML baseline with scikit-learn - maybe TF-IDF features with logistic regression
Move to deep learning with PyTorch or TensorFlow if baseline performance isn't sufficient - fine-tune a BERT model
Evaluate and compare using scikit-learn's metrics even if you trained with PyTorch

Notice the layers: data manipulation (Pandas), numerical operations (NumPy), traditional ML (scikit-learn), deep learning (PyTorch/TensorFlow). Each layer serves a purpose.

When to Use What

The most important decision isn't TensorFlow vs PyTorch - it's traditional ML vs deep learning. This determines which tools you need.

Use traditional ML (scikit-learn, XGBoost) when:

Your data is structured/tabular with clear features
You have hundreds to tens of thousands of examples (not millions)
You need fast training and iteration (seconds to minutes)
You need interpretable models (understanding why a prediction was made)
You're building your first ML model on a problem
Examples: fraud detection, customer churn prediction, demand forecasting, pricing optimization

Use deep learning (TensorFlow, PyTorch) when:

You're working with unstructured data (images, text, audio, video)
You have large datasets (hundreds of thousands to millions of examples)
You're fine-tuning pre-trained models (like LLMs)
You need to learn features automatically from raw data
Performance matters more than interpretability
Examples: image classification, speech recognition, LLM fine-tuning, recommendation systems with complex interactions

Within deep learning, choose PyTorch when:

You're experimenting, researching, or prototyping
Your team consists of software engineers learning ML
You need debugging flexibility and rapid iteration
You're following recent research (most papers use PyTorch)
You're fine-tuning LLMs or working with transformer models

Within deep learning, choose TensorFlow when:

You need mature deployment on mobile/edge devices (LiteRT)
Your organization already has TensorFlow infrastructure
You need the most battle-tested production tools
You're deploying to browser environments (TensorFlow.js)

Many teams start with PyTorch for development and use conversion tools for specialized deployment needs.

Learning Curve and Daily Use

Easiest to hardest for developers without data science backgrounds:

Pandas (few days) - similar to SQL or spreadsheet operations, intuitive for anyone handling data
NumPy (few days) - array operations are straightforward once you understand vectorization
Scikit-learn (1-2 weeks) - consistent API makes it easy to try different algorithms, but understanding which algorithm for which problem takes longer
PyTorch (2-4 weeks) - modern, Pythonic API, but requires understanding neural network concepts
TensorFlow/Keras (2-4 weeks) - similar to PyTorch in time, slightly steeper curve due to some legacy complexity
XGBoost (1-2 weeks) - easy to use, but tuning hyperparameters requires experimentation

These timelines assume you're learning by working on real problems. Understanding when to use each tool - the conceptual knowledge - takes longer than learning syntax.

Daily use complexity differs too. Scikit-learn and Pandas remain straightforward - you reference documentation frequently but workflows are predictable. PyTorch and TensorFlow require more mental overhead: managing GPU memory, debugging training loops, handling data loading pipelines. XGBoost sits in between: simple for basic use, complex when optimizing.

Capabilities and Trade-offs

Scikit-learn:

Strengths: comprehensive traditional ML algorithms, consistent API, excellent documentation
Limitations: CPU-only (no GPU acceleration), not designed for neural networks or massive datasets
Scale: handles datasets up to a few hundred thousand rows comfortably

XGBoost:

Strengths: state-of-the-art performance on tabular data, GPU support, handles missing values well
Limitations: specialized to gradient boosting, requires careful tuning, less interpretable than simple models
Scale: handles millions of rows, but training time scales with data size

TensorFlow:

Strengths: production deployment tools, runs everywhere (mobile, browser, server), mature ecosystem
Limitations: steeper learning curve, more complex debugging, some legacy complexity
Scale: designed for massive datasets and distributed training across clusters

PyTorch:

Strengths: intuitive debugging, Pythonic API, dominant in research, increasingly strong deployment tools
Limitations: historically weaker mobile deployment (improving), less mature than TensorFlow for some production scenarios
Scale: same as TensorFlow - handles massive datasets and distributed training

NumPy/Pandas:

Strengths: foundational tools everyone needs, excellent documentation, intuitive operations
Limitations: not ML libraries - they prepare data but don't train models
Scale: Pandas struggles with datasets larger than memory (use Dask or Polars for bigger data)

Notable Alternatives and Specialized Tools

JAX is emerging as a modern alternative to TensorFlow and PyTorch. It combines NumPy-like syntax with automatic differentiation and automatic parallelization. JAX appeals to researchers who want more control and performance, but it's lower-level than PyTorch - you often build your own training loops rather than using high-level APIs. Consider JAX if you're doing research or need maximum performance and control.

Hugging Face Transformers sits on top of PyTorch and TensorFlow, providing pre-trained models and simple APIs for working with modern NLP models (BERT, GPT, etc.). If you're working with LLMs or transformers, you'll likely use this library regardless of the underlying framework.

FastAI wraps PyTorch with even higher-level APIs designed for rapid experimentation. It's excellent for prototyping but less commonly used in production. Think of it as Rails to PyTorch's Ruby - opinionated and productive, but less common in production environments.

MLX is Apple's recently released framework optimized for Apple Silicon. It's worth watching if you develop on Mac hardware, but it's too new to recommend for production use.

What You Should Do Next?

Start with the problem, not the framework. If you're working with structured data, begin with scikit-learn. Spend a week building a baseline model and understanding your data. Only move to deep learning if your problem genuinely needs it - many don't.

If you do need deep learning, learn PyTorch unless you have specific reasons to use TensorFlow (existing infrastructure, mobile deployment requirements). PyTorch's learning curve is gentler and its community is more active for most use cases today.

Regardless of framework, learn Pandas and NumPy first - you'll use them constantly for data manipulation and preprocessing. These skills transfer across all ML work.

The frameworks matter less than understanding when to use which approach. The best developers know whether their problem needs traditional ML or deep learning, and can articulate why. Learn the concepts first, then let the specific framework be the tool that implements those concepts.

Choose frameworks based on your problem, not hype. Most ML problems are simpler than you think - they just need the right tool applied correctly.