The PHP Machine Learning Ecosystem: A Practical Overview

When people say “there is no machine learning in PHP”, they usually mix up two very different things.

It’s true that PHP is rarely used to train large neural networks from scratch. But PHP has been living comfortably for years in the world of model application, vector operations, statistics, classification, embeddings, and numerical computing.

The PHP ML ecosystem is not loud — but it is mature.

It consists of four layers:

1. Classical ML libraries
2. Mathematical foundations
3. Integration tools for modern ML systems
4. Integration with external ML services

Let’s walk through them step by step.

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Classical Machine Learning in PHP

We’ll start with libraries that implement ML algorithms directly in PHP, without calling external services.

PHP-ML

Repository: https://github.com/jorgecasas/php-ml Status: Mostly unmaintained

PHP-ML is the traditional entry point into machine learning with PHP. It contains the full classic ML toolbox:

• k-Nearest Neighbors
• Linear and logistic regression
• Naive Bayes
• Support Vector Machines
• Decision trees
• k-means clustering

The philosophy of PHP-ML is important to understand. It does not try to compete with PyTorch or scikit-learn in performance. Its goal is clarity.

The code is easy to read, easy to debug, and easy to explain. From a learning and educational perspective, this is a huge advantage.

A typical use case looks like this:

• You extract features from a database
• Train a simple classification or regression model
• Serialize it
• Use it at runtime without any external ML service

The resulting code feels almost textbook-like — and that’s a feature, not a bug. You can reason about the model and explain the math behind it without hidden magic.

The example looks almost textbook:

use Phpml\Classification\KNearestNeighbors;

$classifier = new KNearestNeighbors();
$classifier->train($samples, $labels);

$result = $classifier->predict([3, 7]);

And this is, in fact, a huge plus. The code is transparent, the model can be debugged, and the mathematical meaning of the algorithm is easy to explain.

If PHP-ML is a textbook, then the next library is an engineering tool.

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Rubix ML

Website & Repository: https://github.com/RubixML Status: Actively maintained

Rubix ML is a full-featured ML framework for PHP. It focuses on production-grade pipelines, not demos.

Key features include:

• Classification, regression, clustering
• First-class Dataset objects
• Transformers and feature scaling
• Model serialization
• Reproducible pipelines

Let’s see how this works in practice. Let’s say we have data for binary classification.

use Rubix\ML\Datasets\Labeled;
use Rubix\ML\Classifiers\KNearestNeighbors;

$samples = [
[170, 65],
[160, 50],
[180, 80],
[175, 70],
];

$labels = ['M', 'F', 'M', 'M'];

$dataset = new Labeled($samples, $labels);

$model = new KNearestNeighbors(3);
$model->train($dataset);

$prediction = $model->predict([[172, 68]]);

One important design decision stands out: You don’t pass raw arrays into models — you work with structured datasets.

This enforces a mental shift from “script hacking” to “ML engineering”.

Rubix is often chosen when a model is not an experiment, but a long-lived part of a production system, where versioning, repeatability, and stability matter.

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Linear Algebra and Numerical Foundations

All machine learning eventually boils down to vectors, matrices, and tensors. PHP has several solid options here.

RubixML/Tensor

Repository: https://github.com/RubixML/Tensor Status: Active

RubixML/Tensor is a low-level linear algebra library optimized specifically for ML workloads.

It provides:

• Tensors and matrices
• Element-wise operations
• Decompositions and transformations

If Rubix ML is the brain, Tensor is the muscle.

This library is crucial when you care about predictable memory usage and performance, not just correctness.

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MathPHP

Repository: https://github.com/markrogoyski/math-php Status: Active

MathPHP is a general-purpose mathematical library written in pure PHP.

It includes:

• Linear algebra
• Statistics
• Probability distributions
• Numerical methods

In ML projects, MathPHP is often used as a supporting foundation: distance metrics, normalization, statistical estimates, hypothesis testing.

It’s especially valuable because it implements math honestly — without hidden optimizations or abstractions.

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NumPower

Repository: https://github.com/RubixML/numpower Status: Active

NumPower is a special case.

It’s a PHP extension for high-performance numerical computing, inspired by NumPy. It uses AVX2 instructions on x86-64 CPUs and supports CUDA for GPU computation.

This answers a common question:

“Can PHP do real scientific computing?”

Yes — if you’re willing to use extensions and specialized infrastructure.

NumPower is relevant when PHP is not just a web layer, but a computational engine.

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NumPHP and SciPhp

• NumPHP: https://numphp.org
• SciPhp: https://sciphp.org Status: Unmaintained

These NumPy-inspired libraries are mostly of historical interest today. They show that scientific computing ideas in PHP existed long before the current AI hype.

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Modern ML Integrations: Tokens, Embeddings, Data Pipelines

Modern ML rarely lives in isolation. Around every model, there’s infrastructure.

tiktoken-php

Repository: https://github.com/yethee/tiktoken-php Status: Active

tiktoken-php is a PHP port of OpenAI’s tokenizer.

It allows you to:

• Count tokens
• Split text correctly
• Estimate request costs
• Control context length

If you work with GPT, Claude, or Gemini from PHP, this library is almost mandatory.

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Rindow Math Matrix

Repository: https://github.com/rindow/rindow-math-matrix Status: Active

A linear algebra library focused on ML and numerical methods, often used within the Rindow ecosystem.

A good choice if you want strict mathematical APIs and precise control over numerical behavior.

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Flow PHP

Repository: https://github.com/flow-php/flow Status: Active

Flow PHP is not an ML library — it’s a data processing framework.

It handles:

• ETL pipelines
• Data transformations
• Validation
• Streaming workflows

In real ML systems, data preparation is often harder than modeling. Flow PHP fills the gap between “raw data exists” and “the model can consume it”.

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Integration with External ML Services

The most common way to use ML in PHP today is inference via APIs.

LLM APIs: OpenAI, Anthropic, Gemini

PHP SDKs and HTTP clients allow PHP applications to consume:

• Embeddings
• Classification
• Text generation
• Summarization
• Structured data extraction

Architecturally, this means:

• The model lives elsewhere
• PHP controls when and why it’s used

This plays perfectly to PHP’s strengths: queues, databases, caching, billing, UI, and orchestration.

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ONNX Runtime and Model Inference

ONNX deserves special mention.

Models can be trained in Python, exported to ONNX, and executed from PHP via extensions or external runtimes.

This is a rare but powerful setup: no Python in production, but full control over inference inside PHP applications.

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Computer Vision and Signal Processing

PHP is not a leader here, but basic tooling exists.

OpenCV can be used via bindings or CLI calls, with PHP acting as the orchestration layer. This pattern is typical: PHP doesn’t do the heavy math — it coordinates it.

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How to Read the PHP ML Ecosystem as a Whole

One key conclusion matters.

PHP is not a language for ML benchmarks or Kaggle competitions.

It’s a language for connecting machine learning to real products.

Its ML ecosystem prioritizes:

• Code clarity
• Integration
• Data control
• Predictable behavior

If you understand the math behind a model, PHP gives you enough tools to use it in production.

In that sense, PHP’s ML ecosystem is not weak — it’s honest and practical.

PHP and machine learning are about roles in a system:

• Applying models
• Working with embeddings and vectors
• Classification and ranking
• Orchestrating ML services
• Connecting math with business logic

That’s why PHP’s ML ecosystem isn’t a monolith — it’s a precise toolbox.

And for many real-world systems, that’s exactly what you want.

For a broader view of the PHP machine learning ecosystem and ongoing experiments, you can explore Awesome PHP ML—a curated collection of libraries, tools, and projects focused on machine learning in PHP. It’s a great starting point for discovering what’s currently available and actively developed: https://github.com/apphp/awesome-php-ml