Companies across industries are actively investing in AI to gain a competitive edge and unlock new revenue streams. And the numbers back it up: the global AI market is expected to surpass $500 billion by 2027, driven by real-world applications that generate measurable ROI.
But while the business potential is clear, the technical path to getting there isn’t always straightforward. Machine learning is still a relatively young and complex field. There’s no all-purpose tech stack, and choosing the right tools is a strategic decision. Usually, Python programming language is the obvious starting point. Rich ecosystem and wide adoption have made it the default option for AI and ML development.
However, it’s no secret that Golang is emerging as a contender. Known for its speed, and ability to handle large-scale, high-load systems, Go is gaining traction among companies looking to bring AI models into production more efficiently.
In this article, we’ll take a look at the current AI market landscape and explore whether Go is ready for the spotlight in machine learning. We’ll walk through the key ML libraries in Go, compare it to Python, and help you understand which language might be the better choice for your next project.
Contents
- Industry Overview & Market Outlook
- Golang and AI: A Perfect Match?
- A Quick Guide to Go’s Libraries
- Why Python Dominates Machine Learning
- Golang vs Python: A Comparison for Machine Learning
- Conclusion
Industry Overview & Market Outlook
Machine learning is a type of artificial intelligence which lets computers learn from data without needing specific instructions. In other words, instead of following rigid rules, these systems find patterns in lots of information and use them to make decisions or predict things.
What Role Does It Play in Business?
The Machine Learning as a Service (MLaaS) market is growing like crazy at a rate of 35.58% per year. Tech companies, businesses and even governments invest in AI solutions.
A major reason for the market’s growth is the rapid shift to cloud-based services. The Flexera 2025 State of the Cloud Report shows that 73% of businesses were using hybrid cloud environments in 2024. Cloud platforms eliminate the need for big investments in infrastructure but still provide the power needed for big data and complex ML models. This means that businesses can now scale their machine learning projects at a cheaper price.
We’re seeing changes happening in different industries.
Financial institutions are using MLaaS platforms for checking credit, automated trading, AI chatbots, and regulatory compliance support. N26, a leading European mobile bank, implemented an AI assistant to manage customer inquiries. This AI solution handles approximately 20% of customer service requests, thus reducing the workload on human agents.
Manufacturing is also benefiting. Companies like Volkswagen Group are using machine learning to enhance AI for speech recognition, optimizing electric vehicle charging, and predictive maintenance. The last one is the fastest-growing segment, which is expected to grow at a rate of 39% per annum because of the rise of IoT devices and industrial sensors.
Even farming is changing. In July 2024, Google launched an Agricultural Understanding platform for Indian farmers that uses satellite images and AI to improve crop yields and supply chain access.
The healthcare sector is quickly adopting machine learning for disease diagnosis, drug discovery, predictive patient care, and hospital resource optimization. Due to learning, computers can now find cancerous tumors early, and in some cases, even more accurately than experienced doctors. Google Health’s AI system has demonstrated the ability to outperform radiologists in identifying breast cancer in mammograms. Mayo Clinic, one of the leading nonprofit medical institutions in the U.S., is leveraging machine learning algorithms to analyze ultrasound and molecular imaging data to distinguish malignant from benign tumors with accuracy rates as high as 97–100%. This helps reduce unnecessary biopsies and accelerates diagnosis.
The biggest segment is marketing and advertising, where machine learning helps analyze extensive customer information, and deliver simultaneous, targeted campaigns. One of the popular use cases is building recommendation systems. You’ve probably seen phrases like “Customers Also Viewed” pop up on a product page. Or when you’re browsing a movie or book, you start getting suggestions for similar titles, or ones that people with similar preferences have watched or read. These recommendation systems work thanks to AI algorithms.
So, we see that machine learning is not just an experiment. It’s becoming a must-have tool for entrepreneurship and is changing industries around the world. ML and AI projects need infrastructure that can process big data and complex algorithms. Maintaining such systems requires powerful cloud environments and concurrent languages like Go and Python, capable of handling high loads and ensuring smooth operation.
With over 12 years of hands-on experience delivering software across industries, our tech experts bring deep AI domain knowledge and technical insight to the table. We align technology choices with your business goals, data strategy, and growth roadmap to ensure long-term scalability, performance, and ROI. You can estimate your project cost at this link.
Golang and AI: A Perfect Match?
AI applications need languages that are fast, efficient with resources, and able to manage multiple processes at the same time. They should offer clean concurrency support, reliable memory management, and quick compilation to streamline development and deployment.
Golang, or Go, was created to simplify building complex backend (the behind-the-scenes part of apps and sites). Nowadays it’s used for what’s called system-level programming, which basically means creating the core tools and services that keep large, high-load systems running smoothly.
What makes Go stand out is how efficient and easy to work with it is. It has a simple, clean syntax that’s easy to read and maintain. Moreover, thanks to fast compilation times, developers can turn their code into a working program in seconds instead of minutes, which speeds up testing and delivery. The article What Is Go Programming Language: An Extensive Guide for Golang Web Development tells more about this topic.
While Python has long dominated the field, Golang isn’t traditionally the first thing that comes to mind for artificial intelligence and machine learning tasks, but it’s quickly gaining ground thanks to its standout features. Let’s take a look at what makes it a contender.
Concurrency Is a Defining Factor in Machine Learning Infrastructure
AI applications need to juggle several tasks at once: analyze data, handle user requests, and generate results. A good programming language should be able to manage these tasks without one slowing down the others. This ability is called concurrency support.
This is one of Go’s biggest selling points. Go permits to run multiple tasks simultaneously thanks to its built-in goroutines. These are lightweight, independent operations that can run alongside the main program without waiting for each other to finish.
They don’t take up much memory and can run independently. On multi-core processors, different goroutines can work on separate cores in parallel. Goroutines can easily send messages to one another, which helps them stay organized and avoid getting in each other’s way.
It’s possible to build this kind of multitasking in other languages like Python (using modern tools like asyncio), but the fact that Go was created with this capability from the start makes it a natural fit for the field.
Speed & Resource Efficiency
AI and machine learning systems process massive amounts of data. If a language is slow, the entire application can lag or require expensive hardware to run smoothly. So it’s important for a language to be fast and efficient with memory and processing power.
Golang was designed for performance. It’s a compiled language, which means the code is turned directly into machine code before it runs, that’s much faster than interpreted languages. Go also has a simple, lightweight runtime, which helps AI and ML apps process large data loads efficiently without eating up unnecessary system resources.
Caleb Kaiser, a member of the Founding Team at Cortex, shared that their team initially started building their command-line interface (CLI) in Python. But when they tried to move it to other systems or platforms, it became a hassle because Python required extra setup each time. When they switched to Go, it made life much easier because Go creates one single file that works on its own. That made it much simpler to share, install, and run the program on different computers.
Another reason for Go’s rising popularity in this field is its growing ecosystem of libraries. Developers now have access to reliable tools for data processing, training, and deployment built specifically for Go. In the next section, we’ll break down some of the most useful Golang ML libraries and ways they can help bring AI projects to life.
A Quick Guide to Go’s Libraries
The machine learning and artificial intelligence ecosystem in Golang is not as mature as it is in Python, but it’s developing quickly and already offers practical solutions. It’s worth noting that Python has been around since the early 1990s and has been used in scientific and analytical fields for over 20 years, whereas Go is a much younger language (released in 2009) and was originally designed for systems programming and high-performance servers.
So the current gap in ML infrastructure is more a matter of time than a fundamental limitation. Even now, Go libraries make it possible to integrate machine learning and intelligent analytics directly into Go services without having to deploy separate machine learning systems or rely on Python. We suggest taking a look at the three most popular libraries on GitHub.
Golearn
Golearn is a popular library for Golang, which offers tools for working with machine learning models. With Golearn, you can:
- Load and process datasets. For example, you can take a table of customer data and purchase history in CSV format and load it into your program. Golearn reads these files and converts them into a format that’s easy to work with and analyze.
- Split data into training and testing sets. Before training a model, it’s common practice to divide the data. In Golearn, splitting data is handled using the golearn/evaluation package. This split helps avoid overfitting (when a model performs well on the training data but poorly on new data) and gives a more realistic view of how the model will behave in production.
- Train models. Golearn uses several standard machine learning algorithms for training its models. As of now, the library supports Decision Trees (ID3), k-Nearest Neighbors (KNN), and Naive Bayes. First, you create an instance of the model, then pass a prepared training dataset into the Fit or Train method, depending on the model type. The model then builds its internal structure based on the data. Once training is complete, you can use the Predict method to make predictions on new data.
- Evaluate model performance. Once a model is trained, you need to measure how well it works. Golearn provides the evaluation.GetAccuracy function to compare the predicted labels to the actual labels in the test set. This function represents the proportion of correct predictions (accuracy). You can also generate a confusion matrix that shows how many times the model correctly or incorrectly predicted each class.
Gonum
The Gonum library is primarily designed for mathematical, statistical, and scientific computing. However, it can also be useful for ML tasks like data preprocessing and analysis. It’s a good fit when you need to build a simple model or algorithm from scratch (since the library doesn’t offer pre-built ML models), or when working in an environment where using Python isn’t practical.
Gorgonia
This tool lets build and train neural networks, perform automatic differentiation (an algorithm that computes partial derivatives of variables regardless of how those variables are transformed), work with tensors (multidimensional arrays of data), and create computation graphs (a sequence of operations used to compute a value).
Gorgonia integrates easily with backend applications or microservices written in Go, without calling out to Python scripts. That said, it’s a lower-level library compared to Python AI frameworks like TensorFlow or PyTorch, meaning you’ll generally need to write more to accomplish the same tasks.
Why Python Dominates Machine Learning
In a report released in October 2024 by GitHub, the world’s leading platform for hosting and collaborating on software projects, Python was named the top language. It remains a popular choice for work in generative artificial intelligence, a field that has seen rapid growth on the GitHub platform.
Python is a high-level, dynamically typed, interpreted language with clear syntax.
High-level means it’s designed to be closer to human language than to machine code, so it’s easier to read and write. Machine code, which is the only language a computer processor truly understands, consists of combinations of zeros and ones. High-level languages use a more natural syntax that gets translated into machine code by special programs called compilers or interpreters. High-level code can be run on different devices regardless of the processor type.
Interpreted languages execute code line by line during runtime, unlike compiled languages, which require the code to be fully translated into machine code before it can be executed. Dynamic typing means you don’t have to declare the type of every variable, Python figures it out while the program runs.
These features are a big part of why Python has become a favourite language for researchers and scientists working on AI and ML. Its ecosystem has grown steadily over the years, supported by an active community and contributions from leading tech companies. Let’s take a closer look at some of the most widely used tools and frameworks that make Python so powerful.
TensorFlow
This library supports deep learning, a machine learning that uses neural networks. Neural networks are algorithms designed to work like the human brain. They process tons of data to find patterns and make decisions.
TensorFlow helps to perform different types of AI tasks. It’s used for image recognition, natural language processing (NLP), and personalized recommendations. It’s also used for forecasting things like demand or weather. TensorFlow can generate all types of content. You can train and run TensorFlow models on everything from powerful servers to mobile devices.
TensorFlow has a broad ecosystem of tools. TensorFlow Lite enables model deployment on mobile devices and embedded systems, TensorFlow.js brings machine learning capabilities directly into the browser using JavaScript, and TensorBoard helps visualize and debug the process of training. Moreover, TensorFlow supports Keras, a high-level API that simplifies model building and makes deep learning more attainable.
However, there are some points to consider. Compared to PyTorch, it has historically been considered less intuitive. Its vast feature set can be both a strength and a barrier, as the sheer number of options and tools may be overwhelming for beginners.
PyTorch
Another handy tool for deep learning is PyTorch. It allows building models of any complexity. Around the core framework, a rich ecosystem has emerged, made up of many libraries tailored to different machine learning tasks. PyTorch is open-source and primarily maintained by Facebook’s AI team.
The framework is most commonly used in computer vision and image recognition tasks, including object detection and motion tracking. It’s also popular for pattern recognition, data analysis, NLP, speech recognition, and machine translation. Developers use it to generate image captions, analyze text and extract information, and even to generate content.
What really sets PyTorch apart from other ML tools are two key features: it uses dynamic computation graphs, and supports GPU acceleration. It used to be less popular than some alternatives, but it has rapidly gained ground and now rivals TensorFlow in terms of usage and community adoption. One more strength is its extensive, well-written documentation that clearly explains all major concepts and functionalities.
NumPy
This library is primarily used for numerical computations and working with arrays. It offers a vast collection of mathematical functions, it’s like “Excel on steroids” for programmers. NumPy doesn’t perform machine learning on its own, but it plays a key role in data preprocessing, algorithm prototyping and testing. It serves as the foundational layer for many other libraries.
Together, these tools make Python an incredibly well-equipped language for building and scaling AI and ML solutions from early prototyping to full-scale deployment. But it’s not the only option. In the next section, we’ll compare Python and Go head-to-head to see how they stack up in the machine learning space and when it might make sense to choose one over the other.
Golang vs Python: A Comparison for Machine Learning
Each language has its unique strengths and uses. When choosing, consider the volume of data you’ll be working with and whether the code will interact directly with end users or stay within, say, an internal analytics project. It’s also worth considering whether you and your team are ready to explore something new, leveling up like that takes a significant investment of time.
We’ve put together a table outlining the main points to consider for both languages, side by side, to clearly highlight the distinctions.
| Feature | Golang | Python |
| ML Ecosystem Maturity | Limited selection of libraries, focuses on performance and core ML use cases. Still developing. Smaller but active community. | Very mature with a wide range of established ML libraries. Extensive community. |
| Performance | Compiled language (fast, efficient, low memory usage). | Interpreted (slower but sufficient for most ML tasks). |
| Concurrency | Built-in goroutines and channels for easy parallel processing. | Concurrency possible (e.g. via asyncio, multiprocessing) but more complex. |
| Ease of Learning | Clear syntax, but fewer ML-specific resources. | Minimal “noisy” code, there’s no need for curly braces or semicolons, the structure is defined by indentation. The number of options and tools may be overwhelming for beginners. |
| Deployment | Single binary (easy to distribute and run across systems). | Requires virtual environments and dependency management. |
| Scalability | Excellent for building scalable, high-performance backend ML services. | Scales well with external tools and frameworks. |
| Best Use Cases | Real-time inference, production-level performance, system-level ML tasks. | Prototyping, research, academic use, deep learning development. |
If you’re looking to build ML–based software, Python is almost a must for developing ML algorithms. If your goal is to deploy a large-scale API, you’ll also need a robust infrastructure. And in that case, Go might be the better option. Our team can help you choose the right tech stack based on your specific needs.
We’ve had hands-on experience with Go at Rubyroid Labs, putting it to work across projects in travel, real estate, wellness, and beyond. It’s the engine behind a tour management app for SailCroatia, a CRM system for Big Purple Dot, a yoga booking platform for Yoga La Source, and yes, even our own website runs on it. We’ve seen what Go can do in all kinds of settings, and we’re happy to share those insights if you’re thinking of using it in your next build.
Conclusion
We’re only at the beginning of this exciting, large-scale language showdown. Python and Go are constantly improving and adapting to the growing demands of AI and ML systems. As AI becomes a core driver of innovation across industries, the languages supporting this revolution are also stepping up. It’s entirely possible that in a decade, we’ll see not only the rise of both languages but also their unexpected convergence through shared libraries, frameworks, and hybrid solutions.
At Rubyroid Labs, we’ve been building software products for over 12 years, some of them are in the AI and ML spaces. We always select the best tech stacks and combinations for each project to bring your ideas to life in the most effective, reliable, and forward-looking way.
If you’re exploring Golang for your next AI or ML project, take a look at our Go development services to see how we can support your vision.
