[go golang] Golang Machine Learning

Golang Machine Learning

Golang Machine Learning

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Golang (or Go) is a popular statically typed, compiled software development language. Designed and first introduced in 2009, Go is open-source and offers several capabilities making it ideal for creating system-level services. Built as the twenty-first century C programming language, Golang has many features that help developers build solutions. Error-checkingbuilt-in testing, and automated documentation are just some of the many helpful components Go offers.

Big Data and Machine Learning

Go's launch in 2009 coincided with other trends that have shaped the way people work, learn, and communicate. Cloud computing, social media, mobility, and the exponential rise of Big Data have become core technologies that fuel the modern information economy. Together they have created a platform that has generated a vast amount of data. In the past ten years alone, the world has seen an exponential increase in the quantity and quality of information created, copied, captured, and consumed. Today's amount of data has also increased the building and adoption of Artificial Intelligence (AI) and Machine Learning (ML) solutions. As these technologies rely on vast amounts of information to build accurate models, the data created by these core technology waves have spawned the AI innovation we see today.

Golang Machine Learning

Data scientists and AI innovators have created various models to harness the power of Machine Learning. Developers have leveraged these models to build software libraries that you can leverage to assemble a Machine Learning solution. Go, like Python and Java, has several libraries you can utilize for Machine Learning. However, Go is faster than Python. It also has several benefits like ease of use, efficiency, and concurrency, making it better than Java in specific scenarios. The bottom line is that you can use Go to build robust, scalable Machine Learning solutions.

Golang Machine Learning Libraries

Go offers developers several Machine Learning libraries they can incorporate into their solutions. Combined with Go's features, these libraries can enable fast, robust, and efficient Machine Learning platforms.


GoLearn offers the ease of simplicity with the power of customization. This Machine Learning library implements its interfaces in the same manner as Python's Scikit-Learn. In addition, with helper functions for test-splitting and cross-validation, GoLearn provides the relevant resources developers need to manage their data integrity.

The library contains a combination of C++ and Golang code. However, native Go code is the predominant language, with C++ only used for the library's linear model. This Golang support enables developers to interact with many components directly, resulting in faster and more efficient code.


Gorgonia is a Machine Learning graph computation-based library. It is built for performance and can scale across multiple servers. It also provides a platform for the exploration of non-standard deep-learning and neural network solutions. Some of the features it offers include neo-Hebbian learning, corner-cutting, and evolutionary algorithms.

Built on the Go platform, Gorgonia offers a seamless environment for Golang developers. The library's creators designed the platform to bridge the gap between the two processes of a typical Machine Learning solution, the experimental and deployment phases. Standard ML solutions use Python for the first stage and then refactor the code into C++ or something similar for performance. As Go is faster than Python and built on the same syntax as C++, the Gorgonia library simplifies this process with a single codebase for both phases.


eaopt is another Machine Learning library written in Go. This solution leverages evolutionary optimization algorithms that minimize or maximize a function without using gradient information. eaopt offers several evolutionary algorithms that utilize a consistent API. This capability means that developers can write a single Machine Learning use case in Go and seamlessly integrate it with all the algorithms in the library.

This Go Machine Learning library also offers speciation and migration procedures. In addition, it provides native support for familiar genetic operators such as mutation, crossover, and selection. eaopt also offers parallel function evaluation that developers can use to improve the performance of their solution.


GoMind is a neural network Machine Learning library. Written entirely in Go, this platform learns from a training set that uses a back-propagation algorithm. It supports activation functions such as Sigmoid, ReLU, and Leaky ReLU and can estimate the ideal number of hidden layer neurons.

GoMind does not have the depth and features of the more popular Machine Learning platforms like Scikit-Learn or Tensorflow. However, it is a sound library that offers a simple platform for training. Consequently, it allows Go developers to start Machine Learning without worrying about creating Python models and binding them with C or C++.


goml is another Machine Learning library written in Golang. Its format and structure enable developers to integrate Machine Learning directly into their solutions. It offers several ML models, including generalized linear models, perception, clustering, and text classification.

goml also provides a set of features that simplify the process of integrating a Machine Learning solution. With modular source code, extensive documentation, comprehensive tests, and a clean and expressive code base, goml provides online and reactive learning functionality.


Goga is a genetic Machine Learning library created in Go. Its features allow developers to inject different behaviors into the main genetic algorithm object. Using three injectable components simulators, selectors, and mater, it offers the functionality needed to solve genome-related compatibility issues.

The library comes with several features that help researchers integrate its functionality into their solution, including a string and image matcher. In addition, it can take multiple inputs and present a correlated solution that depicts the union of the various input objects.

Golang Machine Learning Benefits

Software developers, data scientists, and engineers building Machine Learning solutions have the option of using several languages to achieve their goals. However, Go has several capabilities that make it an ideal candidate to build the next ML solution. Go's speed and performance are suitable for the CPU-intensive, high computation calculations AI solutions require. It is faster than Python, easier to use than other Machine Learning platforms, and offers several Machine Learning libraries.



[출처] https://upstackhq.com/blog/golang-machine-learning




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