Google created the Go programming language, often known as Golang, in 2007. Golang, like Java and C++, makes concurrency easy while also giving you lightning-fast performance.

Go has a syntax similar to C, but it has additional capabilities like garbage collection, memory safety, and structural typing. Concurrency refers to the ability for users to run many algorithms or programs at the same time without impacting the output.

With this in mind, we'll take a closer look at Go and consider the merits and drawbacks of using it to build a basic machine learning pipeline. You can use Python as a reference as we go if you're not familiar with Go.

Introduction to Go

Go, also known as GoLang, is a Google-developed open-source, compiled, and statically typed programming language. It's designed to be simple, fast, readable, and efficient.

Every programming language has its own set of benefits and drawbacks, and Go is no different. Go's strengths include its speed, simplicity, and ability to execute code quickly.

At the same time, Go requires more development effort than other Machine languages. One of the reasons Google created Go was to address the existing scalability issue. Previously, resources were bound by hardware type.

Go may be able to solve this problem by allowing for small memory footprints and concurrent functions. Backend engineers are relieved of the stress of dealing with massive queries.

Dropbox is an excellent example: it uses Go to support its network of over 500 million users without trouble. Go is also an excellent solution for building e-commerce websites, as it can manage millions of monthly users.

If you want to learn more about Go basics, you can check out this complete beginner's guide to Go.

Can You Use Go for Machine Learning?

To leverage the potential of Machine Learning algorithms in data science, data scientists and AI pioneers have constructed a variety of models.

These models have been used by developers to construct software libraries that may be used to generate Machine Learning solutions. Machine learning libraries, like those in Python and Java, are available in Go.

In contrast to Python, the Go machine learning library is simpler to understand and use. It also has a number of advantages over Java, including ease of use, efficiency, and concurrency, making it desirable in certain situations.

Additionally, Golang machine learning contains a number of tools that help data scientists and programmers make the development process simpler and more efficient.

Golang Tools for Data Scientists

These are a few Golang-based tools for Data Scientists and Programmers, which help in automation, and productivity. Some of these tools are:

1. Go fmt

Gofmt is a tool that automatically formats your Go code, making it much easier to read. It uses the standard input if there is no explicit path. It operates on the given file – given a directory, it recursively operates on all.go files in that directory.

2. Go Run

Go rum is used to run source code by adding a bang line, or to explicitly execute a code file that is similar to it. It is extremely helpful for developers.

3. Go get

Goget downloads and saves GitHub libraries to your GoPath for simple integration into your app project. Depending on whether the go command is running in module-aware mode, or legacy GOPATH mode, the go get command behaves differently.

4. Go doc

Godoc generates HTML or plain text documentation from Go source code, including comments. Godoc is a tool for extracting and creating documentation for Go program. It functions as a web server, displaying the documentation as a web page.

Advantages of Go for Machine Learning

Go is increasingly widely used by software engineers to design backend systems. There are various reasons why you might want to use it in your ML projects, including:

1. Go Has Testing Support

In Go, testing assistance is arranged as a standard library. There will be no new dependencies required by users. Creating a new file called thing test and write your test.go, then you can put it into action.

2. Interface Types

Go has a wide range of interfaces, offering programmers more options. The interface can be used by users to construct more modular, testable code.

A first-class function is also included in this language, allowing users to write code in multiple functional styles. ‍

3. Standard Library

When dealing with various primitive types, the Go Standard Library provides a variety of useful built-in functions that will come in handy. Other resources help you with topics such as web server configuration, cryptography, and raw data manipulation. ‍

4. Easier Concurrency Model

In general, concurrent programming is difficult, but it is significantly easier with Go than with other machine languages. It's simple to make a light thread and interact with it through channel, and it also enables more complex patterns.

Other than these important advantages of Golang for Machine Learning, it also have other general advantages. Some of them are discussed below:

5. Golang is Fast

Golang was created with the goal of improving the speed of existing languages at the time. Because it is compiled machine code, it will inevitably exceed virtual run times or be interpreted.

Data is captured quickly and with a small binary in Go applications. API compiles and generates an active file that is slightly larger than 10 MB in just a few seconds on average.

6. Go is Easy to Learn

In comparison to other languages, the grammar of this machine language is short, making it easy to learn. You may memorize the entire language. You won't have to waste time double-checking items, which will speed up your tasks.

Disadvantages of Go for Machine Learning

Go has an easy-to-understand clean syntax, quick execution, and a slew of other advantages, but it isn't without flaws. There are a number of drawbacks for Golang for Machine Learning, including:

1. Absence of Generics

This is Go's main flaw: the language lacks generics, which is a major disadvantage for Java programmers. Because generics aren't available, your code is less likely to be reused.

While Go works well for developing techniques such as reduce, map, and filter that work on a single type of collection, you will not be able to reuse these functions across many collections.

2. Interfaces Are Implicit

It is beneficial to have a user interface. Structs, on the other hand, implicitly rather than explicitly implement interfaces.

This may be one of Go's benefits, but it's tough to tell whether a struct implements an interface just by looking at it. Before you can find out, you'll have to try to develop the software.

3. Fractured Dependency Management

After years of nudging from its members, the Go team finally produced a godep. Developers used a variety of tactics to bridge the gap before the release.

As a result, for data scientists, programmers, and newbies to Go, the community has become divided and confusing. Furthermore, Git is used for almost all package management.

Maven Central, in contrast to Maven Central, which is a more permanent solution, will not remove or update a library that your project relies on.

How to Set Up Go

Let's get Go up and running on your machine now that we know how it works and why it's good for machine learning. Here's a quick guide to install GoLang in 5 minutes.

1. Install Additional Packages Required for Go

Install the GoNum and DataFrame packages, along with any other prerequisites. GoNum and DataFrame are functions that are comparable to NumPy in Python and are used to manipulate DataFrame objects.

2. Install the GoLearn Package

Installing GoLearn is required. GoLearn is a Go machine learning library. It enables simple matrix manipulation, the creation of machine learning algorithms, model fitting, and even data separation for training.

3. Install the Go Application

Now, you can install the Go official application, available here. This is extremely compulsory to use and learn and use Golang. If you are using windows, make sure to update it to the latest version.

Go Applications are 100 MB - 250 MB in file size. Make sure that you have at least 2 GB space available on your disk before you actually get started with installing it.

DataFrame in Go

A DataFrame is a data structure that, like a spreadsheet, organises data into a two-dimensional table of rows and columns.

Because they are a flexible and intuitive way of storing and working with data, DataFrames are one of the most commonly used data structures in modern data analytics.

In this tutorial, we'll use the IRIS dataset, which defines the various types of IRIS flowers Data Set. This data set contains the petal and sepal lengths of three different types of irises (Setosa, Versicolor, and Virginica), stored in a 150 x 4 numpy.ndarray.

The columns are Sepal Length, Sepal Width, Petal Length, and Petal Width, and the rows are the samples.

Data Manipulation in Go

This tutorial will show you how to manipulate data in Go.

1. Subdividing

To begin, the df.head() technique is the simplest fundamental sub setting action in Python. The DataFrame's first two rows are visible. After that, the DataFrame's first two rows will be sss.

This is similar to Python, but it is not as technical as Python. This function, however, may be difficult to use if you are inexperienced with Go's numerous data types.

2. Filtration

Begin by filtering the rows using the following if you only want to look at the Iris-versicolor species' primary genetic algorithm item.

versicolorOnly := df.Filter(dataframe.F{ Colname: " Species", Comparator: "==", Comparando: "Iris-versicolor" }) fmt.Println(versicolorOnly)

Only rows containing the Iris-versicolor species will be returned by this technique! Finally, there is a little difference in the syntax of Go and Python.

Can We Use Go for Data Science?

When it comes to developing neural networks using data science, the first few names that come to mind are Java, TypeScript, C++, and JavaScript. The area of data science, on the other hand, is not limited to these languages.

Other open-source programming languages are rapidly making their way up the list of the best open-source programming languages.

One such programming language that is quickly becoming a developer's data frame structure preference is Go.

Conclusion

To summarize, the Go programming language, also known as Golang, is an ideal language for building a Machine Learning pipeline. And one of its many benefits is its intuitiveness.

This complicates troubleshooting, and the automated differentiation might be irritating. Furthermore, many Go packages lack GPU support, which slows down AI development.

According to my research and knowledge, I would definitely recommend you to learn GoLang for Machine Learning, and Data Science. Though, you have already started learning Python, I would recommend switching.

Nonetheless, parallelism and tremendous speed compensate for this; while not currently there, they have the potential to replace Python in ML applications.