golang best for ai

Golang: Best Language For AI, Outperforming Python

Today, millions of users around the world use Golang as a programming language for machine learning and AI. The best AI programming language is Go, not Python! Yes, let’s read in detail about these two languages. Go will soon be celebrating its 11th birthday, having launched more than a decade ago in November 2009. With […]

Golang is Best Language for AI

20 Jul, 2021 | Web Development

Golang: Best Language For AI, Outperforming Python

Today, millions of users around the world use Golang as a programming language for machine learning and AI.

The best AI programming language is Go, not Python! Yes, let’s read in detail about these two languages.

Go will soon be celebrating its 11th birthday, having launched more than a decade ago in November 2009. With Google’s language, developers are now more productive. The designers of this language were also aiming for easy learning and ease of use as well as eliminating the ‘extraneous garbage’ of languages like C++.

Additionally, it has the capability of compiling machine code, as well as the convenience of garbage collection. The future is very bright for Go.

Promising Advancements in the AI domain

AI, even though it has accelerated its progress in 2020, has proved to be a tremendous challenge for citizens, businesses, and governments around the globe. Our economy and society have been transformed through major advances in the fight against Covid-19. Here are a few industry-specific advancements.


 The application of AI to healthcare applications transformed several organizations. AI has made many new discoveries, such as analyzing patient data, diagnosing accurately, discovering drugs, managing medication, and even installing robotic surgery machines.

Retail and e-commerce 

 Ai enables retailers to provide more personalized experiences to their shoppers by utilizing intelligent algorithms to help them identify products relevant and helpful to their interests, ranging from building unique marketing patterns to attracting a large number of retail customers.


 By making it possible for humans to give instructions and directions to robots, industrial robots are delivering a major revolution. As a result of AI, production machinery can be maintained more effectively, faults can be detected early on, and product quality can be improved.

Banking and Finance 

Using AI assistance, computation time is reduced and real-world suggestions are offered. In addition to detecting fraud and preventing it, AI’s digital intelligence improves customer experience by reviewing critical data features.

Why Golang Machine Learning is Mainstream for Building AI-Powered Apps

Using AI, companies can build a wide range of innovative applications tailored to their needs. The web framework must complement AI, however. Nevertheless, what is so special about Go? Due to Go’s fast and scalable nature, apps built on it are very popular. The popularity of this language among AI developers can be explained by the reasons given below.

It is very easy to identify problems in Golang because it is statically typed and compiled. In addition, because Go compiles machine code in real-time, the editing and refreshing cycle is relatively fast while still resulting in efficient machine code. Thus, Golang machine-learning language is used for many applications. 

This makes Go a great choice for developers since it allows you to write highly concurrent networked programs. There are not many functions in Go to Learn. The Go community is increasing day by day. The underlying infrastructure includes a lot of testing features allowing for better engineering discipline by identifying and testing modules easily.

Go language (GoLang) Framework

Reasons why Golang is Best for Machine Learning

The most noteworthy benefit of Go is its ability to run on multiple platforms, making it a great language for software migration. Furthermore, the Go compiler enforces unused variables for Go and has opinionated formatting built in.

Also unique to Go are its native concurrency and parallelism capabilities. Due to this, Golang is the ideal platform to write concurrency-intensive applications, networking, big data, and more. In particular, Golang is perfect when it comes to cloud compatibility. Further, the Golang ecosystem continues to develop with machine learning, making it a great choice for developers. Also, Golang browser automation makes it the top choice for developers. 

You can start by learning how to use the Go programming language in several ways. With its ability to build cloud-native applications and develop a fast and elegant CLI (Command Line Interface), to support DevOps and SRE (Site Reliability Engineering), implement databases, and build websites, it is ideally suited for such projects.

What is the Go programming language used for?

Uses of Go Language

To build the backend of Google’s applications, developers created the Go programming language. For applications that require high-performance and easily understandable code, it’s an obvious choice.

Shortly put, Go is suitable for a wide range of projects. Quite a few things it offers should interest you, so let’s see what is there.  

The Best Golang Machine Learning Libraries

For Golang, there are open-source machine-learning libraries that are stable and well-supported. The open-source community has endorsed it due to its rapid development process and many well-supported repositories. Golang is an emerging language that is used for machine learning. Let’s examine the most popular Golang Machine Learning libraries.

Google created Golang in 2007 as a compiled programming language designed to be efficient. Among its many advantages are:

  • In comparison with other languages, the build time is extremely fast
  • The performance of the run time is excellent
  • Concurrency support of the highest standard
  • An extensive set of libraries, particularly for machine learning
  • Minimizing unforeseen exceptions through forced error handling
  • As projects grow, the environment is great for adaptability
  • Excellent management of dependencies
  • IDE and debugging simplified
  • Support for native languages

Golang is used as the main language of production systems by some of the world’s most successful technology companies, which actively contribute to its development. Golang is used by Cloudflare in many internal software projects as well as parts of larger projects. Google itself uses Golang internally. As well as non-disruptive background loading, Uber uses Golang extensively for high throughput and low latency.

The company Dailymotion, which has a network of over 250 million people and employs Golang extensively, is a global provider of video streaming services. Asteroid, the company’s Wireguard server management application, is written in Golang. By improving efficiency, the application added and removed access to the company’s infrastructure. Due to its powerful features and libraries, Golang is becoming one of the most powerful open-source machine-learning tools.

Golang Machine learning libraries:-


GoLearn is one of the most important packages in Golang. Many machine learning algorithms can be implemented with it. DBSCAN (Density-Based Spatial Cluster Analysis), random forest (RF), k-nearest neighbors (KNN), Naive Bayes (NB), neural network (NN), and principal component analysis (PCA) comprise the package’s machine learning algorithms. It is necessary to have a compatible compiler installed in order to install this package.


This is a low-level library. Developers must build the architecture for the model. main striking feature of this library is its ability to deal with multi-dimensional arrays easily and efficiently. Another feature is its performance. Building a model takes more steps since it is a lower-level library. It is, however, very simple.


Streaming data of both online and reactive types can be processed by this machine learning library.


In addition to the existing codebase, this package allows you to implement evolutionary optimization algorithms. Each evolutionary algorithm has consistent APIs.


Web services and applications can be written in Golang using the EVO library. This library comes with many UI components. It is easy to use and can be used both on the front- and back end.


For the implementation of neural networks, this package is used. Several activation functions can be implemented – linear, sigmoid, reLU, leaky reLU, etc. This package supports only one hidden layer. While back-propagating, the error is calculated using the means square error function.

Golang Machine Learning for Web and Mobile App Development

You can develop a web or mobile application using Go, as it has a powerful standard library.

This makes Go a perfect platform for developing microservice architecture. We’ll let you know more about that in just a moment!

What exactly makes Go’s architectural structure so admired among app developers? 

Golang for Business

A successful mobile app and a successful web app both require excellent user experiences. Technology is advancing rapidly, and client expectations are growing rapidly as well.

An alternative to traditional web and mobile application development is microservices infrastructure. The idea is to separate complex apps into a plethora of smaller components, each of which is capable of functioning independently. The app’s final version will have substantially improved performance, be more secure, and require lower maintenance. 

Its architecture is distributed, so it can be implemented in a variety of technologies. Using one app, you are able to meet all client needs simultaneously. 

Additionally, it supports a fully scalable Go microservices infrastructure, ensuring the integrity of the application.  The rest of the components may not be affected even if one requires development attention.

Go in cloud-based solutions for business

Cloud computing requires a technology that can deal easily with concurrency issues. 

Programmers take advantage of concurrency by dividing their programs into semi-independent components to speed up development and prevent malfunctions.  

Scalability and resource sharing are the key characteristics of cloud solutions. Data and documents can be accessed and updated without any obstacles. A technology needs to be able to process and store massive amounts of data at the same time, without causing an entire process to stop if one part of it fails. This is precisely why Go’s built-in concurrency makes it ideal for creating scalable Cloud applications. 

Additionally, Go language’s high portability and networking capabilities allow you to take advantage of cloud computing’s benefits. In order to increase the overall scalability, performance, and safety of their systems, companies rewrite their old software with Go. 

Companies such as Dropbox use Go to build their cloud services. The syntax is pretty easy and clean. Also, the written code is easy to understand and the community is very helpful. The proof of the pudding is not in the eating, but you can count on better support, upgrades, and maintenance when global giants support a technology. Hence, Go is a foolproof language for developing cloud-based solutions. 

Why Machine Learning?

In the future, many companies will seek out employees with machine learning skills. They won’t be easy to find, so companies will have to compete to hire the best experts. In addition to great salaries, there are plenty of exciting job offers for machine learning engineers!

More and more companies are turning to artificial intelligence and machine learning. Only 7.9% of programmers in the world specialize in big data and machine learning, according to Stack Overflow Developer Survey 2019 results. 

Humanity’s work on AI is one of its most important endeavors. Fire or electricity can’t compare to it. – Sundar Pichai, CEO of Google.

Go Machine Learning for Data Science

Is Golang a Good Technology for Data Science?

Go will revolutionize AI! Since Artificial Intelligence can only be implemented in a few languages, it is no surprise. Is it a good thing or a bad thing? 

There are many ways in which AI can benefit your business. By leveraging Artificial Intelligence, you can save a lot of money and time on routine processes. Making better business decisions is faster and easier with cognitive technologies. You can minimize the likelihood that you will make a mistake. Through the use of AI, services, and sales can be improved as well. There is no end to the possibilities. Thus, Golang Machine Learning will set a new wave in the industry.

Why is Golang is Best Language for AI?

Python does not have the mathematical capabilities that Go has, as well as the machine learning libraries and algorithms.

In addition to its support for parallelism with Goroutines, it also deals with memory errors.

If you plan to hire Go developers for AI-based applications, here are the key characteristics and functions to pay attention to:

1. Simple error handling and fast debugging

Artificial intelligence applications must make immediate expenditures on input if they want to match human wisdom and perfection. Our Go developers can help you improve every aspect of the pipeline, from machine learning to extensive knowledge to forcing learning libraries.

Furthermore, Go AI apps offer a variety of advanced features. As a result of this, Golang AI applications are less flaky and easier to debug.

2. Benevolent Golang library

Go is a language in which the library is written, so AI developers aren’t forced to learn another language. It attaches great importance to developer convenience when using the Go library for developing AI applications.

3. Amazing speed and accuracy

With this language, AI developers can manage several parallel requests and increase the velocity of AI applications with speed and accuracy. You can develop fast and accurate Go AI applications with its manageable syntax and parallel model.

4. Scalability and Calculation

It has huge potential in analytical and consistent estimations. A complicated numerical code is faster and easier to answer in GO than in another programming language. This presents Go as an extensible programming language. When working with Go for AI applications, you can instantly access your reports or actions.

5. Wide coverage

There are only a few libraries right now, but since you can build many AI apps by querying several domains, they will continue to grow. GoLearn provides data processing, GoML provides knowledge transmission, and Hector provides paired analysis as AI libraries.

6. Smallest & Readable

Minimalist strategies are used to develop Go’s readable, minimalist algorithms. A minimalist strategy makes the code of Go AI developers concise and easily understandable when they perform artificial intelligence algorithms. The detention will occur in regard to recursive algorithms.

7. Highly concurrent networked programs

Among the best aspects of Go is that it’s designed for writing network codes with high-level parallelization, which makes developers’ work easier. It has many testing frameworks built into it; thus, it is easier to identify and classify modules to develop technical development.

Best Go Application Examples 

Go Programming Language Example

There are quite a few successful businesses already using Go, despite its relative newness.   


Since Google developed Golang for internal purposes, you shouldn’t be surprised. However, it has gone beyond that and has become part of the solutions you probably use every day. Chrome, YouTube, and Google Earth are some of the most notable examples. 


Geofencing and dynamic prices are two functions that Uber relies on Go for. Specifically defined special-requirement areas (such as airports and train stations) are identified by advanced algorithms. Thus, Uber is an excellent example of the capacity of Go.


Cloud computing service provider with more efficient scalability using Go technology. In addition to relying on inadequate Go libraries, Dropbox also developed its own libraries for caching, standard error handling, and more.  


Entertainment apps like Dailymotion can be made more efficient by using Go. The company used Go to automate API calls. In addition to Netflix, many other streaming brands rely on Go for high performance and stability. Thus, this example shows us, what God can do!

Benefits of preferring Golang for AI engineers in a Nutshell:

  • Error debugging and troubleshooting simplified
  • Go libraries curated 
  • Fast and accurate
  • Coverage on a broad scale
  • Ease of scaling

Why Choose Python?

Python Language for AI app development

Use Python programming language for a range of purposes. A beginner will find it easy to learn because of its simple syntax and it’s open source. Thus, writing Python language code is simple. Various libraries are available in Python, making it extremely popular.

In fact, it performs almost every task imaginable, which is one of its best features. Python is the language with the fastest growth today. Developers use it most frequently according to the results of the Stack Overflow Developer Survey. Python finds its use in Data Science applications.

Python’s downsides

Although Python language is open source and sometimes is touted as the best language for AI, it also has some disadvantages.

Bad for large-scale engineering

Python language clearly loses out in scalability to Go for work involving a few hundred programmers. In addition, Python code requires a very ordered and disciplined approach to coding. You should also keep this in mind when you deploy very complex artificial intelligence systems.

The codebase may be difficult to maintain

A variety of libraries are available in Python, as are support for multiple systems and third-party integrations. Python suffers from such variety.

Python language code is said to be hard to maintain by many developers. What do you mean? There are several features missing from Python, including a statically typed language. Python programming language also has a confusing syntax and goes against assumptions made by other languages.  

Configuring certain clusters in data structures may be problematic or work ceases altogether when these conflicts occur in Python language.

Lack of performance and multicore processing

Python’s performance can also be challenging, especially when using CPUs or GPUs. The challenge can be overcome, but it’s mostly a matter of tweaking. Some solutions don’t work for most common applications.

Too many versions of Python are available

This point is difficult for even Python language programmers. There are several issues related to the transition between Python 2 and Python 3. It can also be necessary to install multiple environments simultaneously if you have multiple versions. It can be a mess and cause technical difficulties when you need them working immediately.

Packaging systems add to the confusion of Python developers. Python is difficult to manage and document packaging systems across versions. Since they are broken down in different ways. The installation of multiple environments is required because packaging systems differ in Python.

Golang Vs Python?

GoLang vs Python

Everyone has this doubt, about which is better Python and Go. Both of them are tremendously popular machine-learning languages. The programming language’s very purpose was to help developers maintain and solve problems at a larger scale. There is tough competition between Python and Go.  There is a reason why Go has built-in support for channeling concurrent processes. Python, however, has faced a big challenge when it comes to concurrency. So, for go vs python, Go is the emerging winner. 

Concurrency support has made Go popular in clusters and clouds. Also, Go is definitely faster than Python.

The fact that Python programming is reputed for its readability is undeniable, however, there are times when they just overdo it. There are multiple ways to say the same thing in Python, which can lead to a lot of confusion. The programming rules in Go, however, are very strict. Clearly, Go will be able to accomplish a task better than other programming languages. The code may not be versatile, but some may disagree.

Who cares about core programming? The code should be readable, so that shouldn’t matter. Thus, if you compare Go vs Python, then Go is clearly emerging as a winner. Also, Go is easy to learn, thus there is no issue in going in-depth into the programming language and developing even the intricate solutions.

Python Vs Go

The task of deciding between Golang and Python which language is better can be difficult. However, Golang appears to have an advantage in most cases. Although it shines less brightly than Python right now, it will eventually get there. As a matter of fact, Python reached its current status over twenty years ago. Python remained unnoticed for ten years, and it was only in 2001 that it began to gain popularity.

As a result, between Python vs Go, Go will catch up soon. You might already be doing an excellent job at attracting attention. These programming languages are much in demand and also a point of discussion as to which one to use.

For building a machine learning pipeline, the Go syntax seems intuitive enough to me. I find, however, that Go’s machine-learning community is smaller than that of Python. In some cases, this makes troubleshooting difficult. In addition, many of these packages lack GPU support, which can make AI development more challenging.

Moreover, there doesn’t seem to be much interoperability between the different ML packages. A DataFrame “Instance” class implemented in the GoLearn package may not work well with the native GoNum’s class or even the GoTA’s DataFrame object.

While Go has the potential to overtake Python in Machine Learning applications due to its speed and concurrency, it first needs a sufficient number of AI developers (i.e. critical mass) to do so.

Why Golang and not Python?

Scalability was the very purpose of this language, to allow developers to solve problems on a larger scale. The reason why Golang has built-in support for managing concurrent processes. Python, however, has struggled with concurrency.

The concurrency support that Go provides has led to its widespread acceptance in cluster computing and cloud computing.

Despite its reputation for readability, Python sometimes goes overboard. It is true that Python offers multiple ways to express the same thing over and over again, which can create confusion. By contrast, Go adheres to strict programming rules. It doesn’t allow the import of unwanted libraries, nor does it allow the creation of unnecessary variables.

As a result, Go has a better chance of completing a task. Its lack of versatility, however, may not be acceptable to some. Nevertheless, who cares about core programming? The code should be readable, after all.

Summing up

Why Go is better than Python

As you now know, Go is an advanced programming language that can handle complexity easily, making it ideal for building artificial intelligence applications. If you want to build AI-driven applications that scale highly, Go is the way to go!

Why should you use Go for your company’s projects?

Using Go, you can create lightweight applications with scalability and modern technologies. Startups with an eye on dynamic growth or mature, big companies will find it to be an equally great choice. 

You can always reach out to us for any further questions or if unsure if Go is right for your project. As a team, we’ll come up with a solution that works for you.

contact TechnoBrains for AI development

How Can TechnoBrains Help In Golang Development?

TechnoBrains helps businesses create high-performance websites, cloud-based infrastructure apps, and microservice-based architecture. We are committed to bringing the most complex of software ideas to life with our team of highly skilled developers skilled in Golang and other programming languages.

With our Go developers, you get full-service Golang web app development, from unit testing to database creation, online games to grid computing to AI/ML apps. 


Why is Go the best language for AI?

Now, millions of users around the world use Golang for machine learning and artificial intelligence. While Python is a great language for programming AI, Golang is even better!

Is go a good language for machine learning?

Python is a much more complex process than this one. Additionally, learning Go is extremely straightforward. In addition to its fast execution (much faster than Python) and excellent concurrency functionality, this programming language is also very easy to learn. The binary form of Go makes it a good candidate for machine learning.

Is Golang good for deep learning?

Designed by Google in 2007, Go or Golang has similarities to C, it is memory safe, has garbage collection, and is structurally typed. Additionally, Go is lightning quick, unlike Python, and supports concurrency just like C++ or Java. 

Could Golang be the next big thing?

Its simplicity and reliability make it a good choice for programming. Microservices are easy to create with Golang, so such programmers will be in demand in the future. The future of programming lies with Golang, no doubt about that

What are the uses of Golang?

Known as GoLang, this robust language is often used for programming in large-scale networks and distributed systems. In the context of what Google required for its network servers and distributed systems, Google Golang emerged as an alternative to C++ and Java for app developers.

How does Golang differ from other programming languages like Python?

Among Golang’s best features is its ease of use and clarity. New programmers can easily learn Golang, while veteran programmers can easily read each other’s code because of its advantages.