Machine Learning Frameworks

Machine learning frameworks are software libraries that provide the building blocks for training and deploying ML models. They handle numerical computation, automatic differentiation, model architectures, and hardware acceleration so you can focus on the problem rather than reimplementing algorithms from scratch.

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Machine Learning Frameworks

A machine learning framework is a software library that provides the primitives for building, training, and deploying models. Instead of coding gradient descent, tensor operations, and GPU dispatch from scratch, you use a framework that handles the mechanics so you can focus on model design and data.

The framework landscape divides into two broad tiers: classical ML libraries for tabular data and structured problems, and deep learning frameworks for neural networks on images, text, audio, and sequences.

Why frameworks exist

Training a model requires computing gradients of a loss function with respect to millions of parameters, distributing computation across hardware accelerators, and managing data pipelines that feed batches efficiently. Reimplementing these from scratch for every project is impractical.

Frameworks give you:

Automatic differentiation for gradient computation.
Optimized tensor operations on CPU, GPU, and TPU.
Pre-built layers, optimizers, and loss functions.
Data loading and augmentation pipelines.
Model serialization and deployment tools.
Community-contributed model architectures and pretrained weights.

scikit-learn: classical machine learning

scikit-learn is the standard Python library for classical ML. It covers supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model selection, and preprocessing.

Machine Learning Frameworks

Why frameworks exist

scikit-learn: classical machine learning

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