Golang for AI solutions: Best Practices and Case Studies
Image Source

Golang for AI App Development: Best Practices and Case Studies

17 min read


How good is Golang for AI? How promising, in particular, is the combination of Machine learning (ML) with Golang? In this article, you will learn when to use Golang for building AI-based apps. We will also analyze specific cases of Golang-backed AI software development.

Three Possible Ways to Use Golang for AI

In discussing the inclusion of Golang in the tech stack of AI projects, we will focus on two main things. 

  1. First, what are Golang’s strengths, and how exactly do they facilitate such projects? 
  2. Second, what kind of toolkit does this programming language have for developing AI solutions?

We will mention certain tools and features when describing Golang usage in different subfields of AI. 

There are a lot of stereotypes and prejudices surrounding the issue of sufficient Go language libraries. This is explained by the language’s young age. Golang appeared recently and, therefore, had less time for the emergence of solutions that developers needed.

However, it is not superfluous to touch on the prospects of increasing various ready-made solutions for Golang. We can note that, in our opinion, Golang has everything for the rapid expansion of the spectrum of frameworks, libraries, packages, and other elements of the language infrastructure. Google has developed and continues to maintain this programming language. The popularity of technology also has a positive effect. Let’s turn to the relevant data. 

Golang is confidently among the top 15 most popular programming languages. Go also enjoys increased attention from developers in the context of its study. It follows that an active expert community has formed around Golang. This is also a sign of the impetuous preparation of the necessary tools in the top areas of software development.

Data preparation is one of the essential aspects of AI. For accurate predictions, specialists preprocess the data they use. For this, in particular, they detect and remove outliers and irrelevant data. It is also crucial to convert the data into a format suitable for model training. 

Golang is well-suited to preprocessing and manipulating large amounts of data for machine learning apps. Processing and analyzing data in Golang is possible with the help of Go packages such as DataFrame.

Now, let’s review three possible ways to use Golang for AI.

1. Natural Language Processing (NLP)

NLP apps, such as chatbots or language translators, might be built with Golang too. Go-nlp is a popular package for NLP tasks, such as tokenizing, stemming, and part-of-speech tagging. Golang’s concurrency feature is just what you need for creating NLP apps.

2. Computer Vision

Go is applied to develop computer vision apps like object detection and image recognition. The GoCV package is a widespread computer vision library in Golang that uses OpenCV. The Go language is increasingly in demand for solving computer vision issues such as object detection and recognition, as well as image classification.

The high performance of Golang is very relevant for developing computer vision apps for which this parameter is critical.

3. Machine Learning with Golang: Reference Points

Golang is an increasingly popular language for the development of machine learning algorithms or models. It can be used to train and run machine learning algorithms such as neural networks and decision trees. 

Go can also be used to develop reinforcement learning applications, such as game-playing agents. The goNEAT package is an implementation of the NEAT algorithm used for evolving artificial neural networks for reinforcement learning.

Golang is impressively flexing its ML muscles:

  • Kubeflow is an open source machine learning platform for Kubernetes that utilizes Go in its deployment and management of machine learning models. Currently, Kubeflow has developed into a platform not only for machine learning, but also for MLOps (deploy and maintain ML models in production). Kubeflow has software modules for each of the stages of a typical machine learning lifecycle: Kubeflow Notebooks for model development, Kubeflow Pipelines and Kubeflow Training Operator for model training, KServe for model maintenance, and Katib for automated machine learning. You can deploy each of the required Kubeflow components separately.
  • OpenAI Gym is an environment for developing and comparing reinforcement learning algorithms. The Go packages like gym-http-api are used to interact with the OpenAI Gym API.
  • The GoLearn package is a specialized machine-learning library for Go offers a wide range of algorithms for data mining and classification. 
  • GoML is a library of ML algorithms that developers can use to build products that combine machine learning with Golang.

Additionally, Golang’s simplicity and performance make it a popular choice for implementing deep learning algorithms. There are many successful examples of Deep Learning with Golang. For instance, the popular deep learning framework TensorFlow provides a Golang binding for its library. Gorgonia significantly expands the capabilities of deep learning in Golang. This library contains what is needed for creating and training machine learning (e. g. deep learning) models.

As you can see, there is significant potential for Machine Learning with Golang. Now, we can already state that until recently, common questions like “Is Golang good for Machine Learning?” and “Can Golang be used for Machine Learning?” are replaced by more relevant questions such as, “How to use the advantages of Golang for Machine Learning most profitably?”. There is reason to believe that Golang can play a crucial role in the development of ML apps in the years to come due to its continuous advancement and the ongoing growth of this AI subfield.

Main 7 AI-Friendly Golang Features 

What makes choosing Golang for AI so relevant when it comes to building apps? AI is powered by data. It follows that AI products require technologies capable of processing large volumes of data quickly enough. This is precisely what is characteristic of Golang AI software. In the Go language, you can build an infrastructure in which the AI module operates as best as possible. Here are the Golang anchor points for your AI-powered app functionality:

1. High concurrency 

In Golang, concurrency is built-in right out of the box. The language was designed from the beginning to create software systems to handle large numbers of concurrent requests and processes, and it does a great job of that. High concurrency is based on specific goroutines and channels and allows processing large volumes of data without latency. Such a feature definitely makes Golang for AI convenient.

2. High-Performance

Golang is a compiled language, which means that code written in Golang is compiled directly into machine code that can be executed faster than interpreted languages. Such a feature makes Golang well-suited for building high-performance applications that can handle large amounts of data.

Golang can perform complex computing 20-30 times faster than many other programming languages. Thus, this technology is relevant for computationally intensive AI models, particularly machine learning.

3. Suitability for creating real-time AI apps

Golang’s low-level networking capabilities make it well-suited for building real-time web applications that require low latency. Some AI-based killer features only make sense when operating in real-time. For example, image and speech recognition should not occur with a significant delay. Concurrency and fast performance ensure that Golang is among the technologies suitable for creating such apps.

4. A wide range of ready-made solutions for facilitating the development of AI-based software

In this case, we are not only talking about, for example, algorithm libraries for machine learning in Golang because we will consider them in detail later. The standard and optional libraries of the Go language provide a lot of useful material for developers at all stages of working with data.

The data required for AI models comes from a variety of sources and, naturally, in a variety of formats. Issues of unification and preparation of data for convenient and fast processing are always faced by project teams. That’s why it’s helpful to find packages for working with data formats like CSV, JSON, and XML, even in the Go standard library. This facilitates the gathering of data from different sources and their subsequent processing.

The list of available Go solutions for various, sometimes non-standard, situations can be continued. Sometimes, for example, there is a need to work with uncertain or inaccurate data. The Fuego library supports fuzzy logic in Go, which, in turn, helps to work with such data.

Sometimes apps require the deployment of AI models on various devices. The Gobot library is appropriate in such situations. Its materials can be used in solutions for the Internet of Things and robotics.

5. Cross-platform compatibility 

When product owners and developers have created an impressive AI functionality, then such features of the selected technology that can shorten the time to market come to the fore. Combining AI with Golang in your app’s tech stack, you can develop for any operating system or platform. You will not need additional interpreters or tools. You can quickly compile and run your Golang AI app.

6. The full cycle of data work 

Golang belongs to the list of technologies suitable for working with big data. Concurrency helps a lot with this. This makes data streaming available, for example. Accordingly, you get the opportunity to visualize information in real-time, which greatly advances AI Golang apps. If you need a cloud infrastructure to host data, Go is also a great fit for this.

7. Effective use of resources 

First of all, we are talking about efficient memory management. Another built-in Golang feature is the garbage collector to manage memory allocation and deallocation. Automated memory management is vital for data-intensive AI apps. By the way, the unique subroutines that ensure parallelism occupy surprisingly little memory, just a few KB each. The reliability of the system software also benefits from optimizing the load on the servers because Golang consumes much less memory than other programming languages.

At the moment, Golang is a popular language for building AI applications because of its performance, concurrency features, and easy-to-understand syntax. Here are some ways that Go is used to create AI applications.

Golang for AI: Practical Tips

In this section of the article, we will share our own experience working on a combination of Golang and AI. Having in-house professionals from both AI and Go, our project teams try to get the maximum from these technologies, achieving a synergistic effect. Let’s trace Golang’s advantages in specific cases of such solutions.

It is probably appropriate to start with the formation of the tech stack of AI-based software development projects. For example, as we showed above, Machine learning with Golang is a good choice. But the same can be said about some other languages and frameworks. What speaks in favor of developing a solution with machine learning on Golang?

Today, when you think about using any programming language for AI, especially for ML, you can’t escape its comparison with Python. Why not? Let’s touch on the topic of comparing the capabilities of Python and Golang for AI.

Golang vs Python 

First of all, it is obvious that Python, given its longer existence, has more ready-made solutions for ML and other AI subfields. Such software fragments are collected in popular and convenient libraries. Also, one cannot discount the fact that among the large community of Python programmers, many specialize in AI solutions. This has been the situation in recent years, that many projects, for example ML, are focused on Python, so to speak, by default.

At the same time, it can be observed that the number of ready-made Golang solutions for AI is rapidly increasing. The gap in the volume and quality of libraries between Go and Python is narrowing. Above, we have already shown this using the Golang toolkit and libraries for machine learning as an example. The potential of the Go language for AI-based development is growing in the same directions as Python.

The mentioned Gorgonia is increasing its ML content, becoming comparable to such Python libraries as Theano, Keras, and PyTorch. The same thing happens in other subfields of AI. The functionality offered by Gonum (Go Numerical) for calculations and algorithms is similar to NumPy. Likewise, Gonum/plot’s data visualization capabilities are already approaching those of Seaborn and Matplotlib. Therefore, it can be predicted that when you start your new AI-based app project, the developers will not only be able to write the necessary code on Go from scratch but also select what is needed in Golang packages for AI.

Other situations are also possible. Let’s say you chose an AI software module in a programming language other than Go to build your app. Does this mean that, in this case, Golang will not have a place in the tech stack of your project? That is far from always the case. It depends.

Apart from the actual AI module, other software parts are also essential for a positive customer experience. This is where there is room to take advantage of Golang, which works great in combination with other languages in the tech stack of AI projects.

Golang is a promising choice for those parts of the software that crucially require:

  • High performance, significantly higher than the aforementioned Python
  • Multithreading
  • Fast, powerful calculations using the CPU
  • Economical memory usage, etc

Let’s schematically depict the structure of an AI-based app:

Structure of an AI-based app

The use of fast, compiled Go for the implementation of domain logic, for the integration of third-party solutions, including cloud computing services, and other purposes will enrich the functionality of the AI-powered app.

Golang seems to be specially created for microservice architecture and writing fast, compact software modules for key app features. For example, it is appropriate to write the API of AI-based apps in the Go language. As well as, let’s say, a chat or chatbot with a lot of traffic.

The reasons to mention the Go language become even more compelling if you think of a large-scale AI-based software project. The compactness of the app and the saving of memory consumed by it become critical on large projects. In addition, a large number of simultaneous requests forces you to take care of concurrency, which is also Go’s forte.

Certain app parts can noticeably slow down when handling large data streams and concurrent operations if they are written in a language slower than Golang. It is also worth remembering about the optimization of programmers’ working hours. They can also write quickly in Python, but they need to spend less time optimizing the code in Go. In addition, it is possible to compile a language with dependent modules and libraries in one binary file, which is also convenient.

Golang strictly regulates how to write code. It saves time and effort in large projects. There is no need to develop guidelines or set any rules regarding the code style in a project with many programmers. Most likely, developers will write more or less the same in the case of apps in Golang with AI.

Below, we will talk about product cases where we succeeded in making Python and Golang complement each other.

Golang For AI Projects: Case Studies

Let’s consider some examples of the projects whose main elements of the tech stack are Golang and AI technologies.

Case study #1: Forecasting and recommendation system for the financial sphere

As is well known, information has a significant impact on financial decision-making. Accordingly, tools for the instant processing of textual information are in demand in this domain.

A solution developed by us, aimed at the semantic analysis of information contained in various reports and articles, was added to this toolkit. 

The main project challenges:

  • Search and selection of relevant publications for analysis
  • Identification and use of relationships between actual data and expected trends
  • Fast processing of large volumes of information from various sources. After all, the financial domain is sensitive to speed, as the situation here changes rapidly, and information quickly becomes outdated

Suggested solution:

A software system developed on Golang that processes the facts and figures given in publications from the point of view of how they will affect the price of certain shares. Based on the information processed in real-time, a forecast is made, and a recommendation is generated, for example, regarding the purchase or sale of securities.

Case study #2 : Portal for managing interaction with cloud services 

The product is a portal for developers, DevOps engineers, CTOs, and product owners. The purpose of the portal is to facilitate the management of interactions with leading cloud services such as Amazon, GoogleCloud, Azure, etc. For such a web resource, it is fundamentally important to collect all the functions of cloud use in one place. This approach allows you to optimize the use of resources, and therefore the cost of using them.

The client entrusted the development to us, MobiDev. And our skills in executing projects, the technical stack of which includes both Golang and ML, came in handy in this case.

The main project challenges:

  • The setting up of the machine learning cycle
  • Creating a software solution for data gathering and running the data processing chain
Golang with ML: A Case Study Outline

From stage to stage of the ML life cycle, technologies replaced each other, as if passing the baton. Golang also played a significant role in a certain section of the distance. First, our specialists applied Go to two stages of the machine learning cycle depicted in the figure, in particular, Data Gathering and Data Preparation. It is important to note that the trained model is then operated with Golang.

Suggested solution:

  • Creating a complete machine learning cycle using Python and Go for model training and refinement 
  • Development of high-performance software for data gathering and preparation in the Go language

Thus, Golang has a variety of options for use in AI-powered apps. Speedy performance, multithreading for concurrent programming, and scalability of the Go language make resource-intensive tasks easy. Cross-platform compatibility and debugging possibilities facilitate the deployment and running of AI apps.

Our project teams keep in sight all options for combining AI with Golang. Depending on the project specifics, it is possible to choose Go as the language for writing the bulk of the code in the project or to apply it locally in separate parts of the software.

MobiDev: AI and Golang Expertise in the Same Team

So, as you can see, there are many advantageous use cases for AI development with Golang in different types of apps. Leveraging the strengths of the Go language, it is possible to create solutions combining AI-based features with high performance, powerful calculations, and big data processing.

We have in-house experts in AI, Go, and other programming languages. With such a range of capabilities, we`ll select the tech stack that best suits your product.

If the core of your project is an AI model, contact us. Our expertise will complement your ideas to create a competitive IT product.

Open Contents


Whether you want to develop a new product or update an existing one, we're eager to assist. Call us or fill in the form via CONTACT US.

+1 916 243 0946 (USA/Canada)



How To Build AI-Driven Financial Applications

How To Build AI-Driven Financial Applications – a…


How to Build an AI Assistant: Virtual Assistant Technol…

How to Build a Facial Recognition System for Your App

Guide to Face Detection and Recognition Software Develo…

We will answer you within one business day