Coursera Learner working on a presentation with Coursera logo and
Coursera Learner working on a presentation with Coursera logo and

If you are creating your first ever machine learning project, you’re probably wondering how you can choose a language with so many sound options. Despite Python’s popular language being simple and easy to use, you may consider choosing advanced technology. This article will help you understand the benefits of relying on Go for your machine learning projects.

Why Machine Learning?

Artificial Intelligence and Machine Learning solutions are growing more popular as engineers work towards new approaches. According to a Stack Overflow Survey in 2019, only 7.9% of programmers worldwide have expert-level knowledge and skills surrounding machine learning and big data. (Source)

This extensive gap limits future perspectives in the realm of Machine Learning. According to some experts, 2021 will provide a fantastic boost in the fields of Machine Learning and Artificial Intelligence as these technologies will have faster decision-making abilities than before.

That is why you will find numerous opportunities as a Machine Learning engineer. However, with this little competition in the market, you have a greater chance to fill in a life-changing space. Furthermore, payment will be incredible for machine learning experts.

What is GoLang?

Golang or Go is a language with increasing popularity, especially for Machine Learning projects. Google introduced this language in 2009 with similar syntaxes and arrangements as C. According to claims by many developers, Go is the twenty-first-century version of C.

Why Use Golang?

A decade after the launch, Go is growing extremely popular due to its flexible and easy to understand language. However, there are numerous other benefits of this advanced language. Below, we will understand the tasks you can perform with Golang:

  • Machine learning
  • Big data
  • Command-line scripting
  • Web development
  • Multimedia editing
  • Cloud services
  • Network server applications

GoLang and Machine Learning

GoLang is an efficient, easy to use, and clean language. However, the machine learning community does not support Go as it should have, despite its numerous benefits. For instance, with Python, you have to download a compiler to compile your codes on a workspace. But with GoLang, you do not have to download a compiler. Numerous professional developers are supporting the libraries and use this language for their Machine Learning projects. When you start with the Go for your Machine Learning components, you will have a great future on this track.

Why write ML applications in Go?

GoLang is an excellent language for developers. You can also create a project with ML components by using GoLang. However, there are various limitations. For starters, it does not support Caffe and TensorFlow. But these deep learning frameworks work well with Python. Still, there is some reason to use Go:

  • The runtime and compilation are significantly faster.
  • Easy and fun language to write codes. The interface is cool and organized, so you can feel comfortable using the application.
  • You will have fun with GoRoutine, events, and channel.
  • The mechanism is new and safe for static typing with tremendous flexibility. The interface and dynamic are overwhelming.
  • You will find numerous GoLang libraries increasing your chance to contribute to quality Machine Learning Projects.

Advantages and Disadvantages of Go for Machine Learning Projects

  • Advantages

  1. The infrastructure of the project is remarkable.
  2. You won’t need to interpret all the files as compiled
  3. To provide you with a smart library
  4. Built-in strong security
  5. Embedded testing environment and automatic documentation
  • Disadvantages

  1. It doesn’t have a virtual machine
  2. No versatility
  3. The little collection of libraries.


Benefits ofGoLang for Machine Learning Components

There are numerous benefits of choosing Go for machine learning. The most significant benefit that you should consider about GoLang is that it is a compiled language. Many developers and programmers in Machine Learning went through this language, and they were happy about using GoLang for project development. Below, you will find some benefits of using Go for Machine Learning:

  1. Simplicity

One of the top reasons to use GoLang that it offers simplicity and convenient features. According to some developers, Python is more complicated than Go and a perfect tool for new developers if they want to venture into the world of Machine Learning.

  1. Compilation Capabilities

You can compile GoLang into a single library. This makes GoLang different from Python, which is a non-compliable language. You can link the language to dependent modules and libraries and comprise them in a single binary file. This means that you no longer have to install the dependencies on your server. This language simplifies this problem as you upload your compiled file and start working on a machine learning project.

  1. Faster performance and Concurrency

GoLang saves you memory and CPU by using a resource-efficient tool for Concurrency. You will use Go routines for Concurrency with Go. Now, you save on resources and costs and get to enjoy the enhanced and fast performance.

  1. Native Support

The Go library already includes popular tools for Machine Learning. These tools are built-in, so you do not have to use third-party libraries. This programming language includes artificial native support, so your entire application development process is smooth and quick. If you need help, you can ask the GoLang community.

  1. IDE & Debugging

The most crucial benefit of using Go out of all the other benefits a top-quality IDE or Integrated Development Environment. The developing world is very competitive and agile. However, an IDE will speed up app development, so you do not have to strive in the market. GoLang provides you with amazing plugins and debugging tools, and a comprehensive IDE.

  1. Clear Syntax

GoLang is straightforward and easy because it includes a precise syntax. When you start using this language for development, you won’t find any component holding you back. You can take clear and direct actions and perform tasks efficiently. After analyzing this language’s features and components, you will find it useful, easy, and an ideal choice to create a Machine Learning model.


After understanding all the crucial factors, pros, and cons, you can decide if you want to contribute to the development of GoLang. If you want to create a Machine Learning project for the first time, you can search for tutorials and other information on using this language. However, you will find it comparatively easier and more straightforward than Python. You can also hire a developer with expertise in GoLang so you can instantly create a Machine Learning project.


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