Quick Summary:

The substantial debate of using Python vs Go or otherwise for developing advanced and modern ML applications persist. This blog covers the advantages and disadvantages of using Python for ML as well as, Go for Machine Learning. Binge on and let us know what shall you choose for your next AI/ML project?

Introduction

Anne is a Machine Learning enthusiast, and throughout in her 25 years of programing life, she was coding on Python. She thought there could be no other programming language better than Python for making machines understand how to learn. In my conversation with Anne when I asked, what is Machine learning? She replied,

“Machine learning is a subset of Artificial Intelligence, which, in simple terms, is a science of getting things done by computers without directly programming. This way, the machines(computers) learn on their own by studying algorithms and statistical models.” She was in love with her life, enabling machines to learn; because she had the best tool in hand- Python.

The incredible benefits she accumulated by choosing Python proved to be vital. If you are wondering, “Why Python is best for Machine Learning?” Let us find out the pros and cons of using Python for Machine Learning

Why Use Python for Machine Learning?

1. Simplicity of Consistency

Python is a highly readable language. It is simple for the human mind to understand, and hence it becomes easy to use Python for Machine Learning. Developers find Python easy to learn, so they prefer to use it over any other programming language.

For a big complicated machine learning project, Python is highly suitable because it allows collaborative implementations from multiple developers like Anne. Moreover, it is a general-purpose language, so a set of machine learning tasks can make use of it, and developers can build prototypes to test their AI products.

2. Exclusive Libraries

The most prominent benefit of using Python is the availability of extensive support libraries. Specifically, for AI and ML, Python has the following:

  • Keras, TensorFlow, PyTorch, and Scikit-learn libraries for machine learning algorithms
  • NumPy library for multi-dimensional arrays, matrices, and advance mathematical functions
  • SciPy for linear algebra, optimization, integrations, and stats
  • Pandas for general-purpose data analysis
  • Seaborn and Matplotlib for data visualization

Such tremendous support eliminates the development time and overhead for programming, providing ease to Anne.

3. Established Community

Python has a vast ecosystem, and it has the OSI-approved open-source license, which makes it free to use and distribute. The vibrant and active community members are continuously collaborating by creating new libraries, updating documentation, and extending the toolset.

Disadvantages of using Python for ML:

1. Restricted Speed:

Because Python is an interpreted language, your ML program may execute slowly line by line. Now, when we want futuristic AI/ML applications, then how can we compromise on the speed?

2. Not innate for Mobile environments:

Python is not considered a strong language for mobile computing. This is the reason why Android and iOS do not support Python for their official programming.

3. Not Suitable for Large Enterprises:

The database access layer of Python is quite underdeveloped when compared to JDBC and ODBC. Hence it is not an ideal choice when developing applications for big enterprises.

The Things transformed, obviously for a reason

If we compare the Google trends of both these programming languages in the last 5 years, here are the results. Of course, Python is proven and here for a long time, so the difference in the graph. Go is quite new but has immense potential for ML.

Anne was in total trance using the best programming language for Golang Machine Learning. Then suddenly, something happened when she got to know that Golang is going to overpower Python. All her thoughts and beliefs regarding Python changed. It happened as she came to know about the Golang programming language (Go). Golang is comparatively new. It was launched a decade back in 2009 by Google. NOT because Python is an older one, but just because of Go’s advancement.

Why Should You choose Go over Python?

Go is a compiled language, and there are significant advantages of Gloang development services when Anne gets to know about developers’ new programming inclination towards Go, she tried her hands on this new programming language for ML, and the results were outstanding.

1. Simplicity

The main plus-point of Go is its simplicity. Anne thought that Python was simple & easy only until she used Go because Go is simpler than Python.

2. Compilation Capabilities

Golang is a compiled language, and it compiles into a single library. It is a statically typed language, and it links all the dependency libraries and modules into a single binary file.

Here, developers like Anne need not install dependencies on the server. Instead, she just had to upload a compiled file for her app to start working.

3. Concurrency and Faster performance

Go uses goroutines for concurrency, which is a resource-efficient way to save CPU and memory. Anne is super-impressed by the faster performance of Go as it saves costs and resources.

4. Native Support

Most of the tools needed for ML are already in-built into the Go library, so developers don’t look for third-party libraries. Go programming language has superficial native support for tools that speed up the entire process of app development. Also, the Go community is getting strong and reliable for any kind of help.

5. IDE & Debugging

A significant advantage of using Go is getting a top-notch Integrated Development Environment (IDE). Anne and most developers feel that in an agile, competitive world, an IDE is the most crucial aspect for development as it can speed-up or hinder the process. Golang comes with a comprehensive IDE and excellent debugging tools and plugins.

6. Clear Syntax

The Go language has a precise syntax, which makes it easy and straightforward. No unnecessary components are holding back the developers on the structure. Instead, development becomes direct and clear.

Are you convinced opting for Golang over Python for your next ML project?
Hire Golang developer from us and leverage the perks of this modern charismatic programming language.

Knowing such ease and usefulness of Go, Anne thought, Go seems to be an ideal choice for machine learning development services and she started implementing ML. And here’s what she found:

What are the Benefits of Machine Learning With Golang?

Benefits of using Go for MAchine Learning

1. AI Libraries

As mentioned earlier, Golang doesn’t require additional libraries; it writes everything in core. Though it provides lesser libraries, those specific libraries cover a broad scope of purposes. The GoLearn library covers data handling; Hector covers binary classification problems; Theano is similar to TensorFlow, and Goml for passing data.

However, Go still needs to work on its toolkit development. It is still in progress and has already set up a good community on GitHub.

2. Exceptional Computation Speed

Math computations are like cake pieces for Go. Unlike Python, large-scale projects are better suited to Go owing to its scalability and performance. When it comes to complex AI computational math programs, Go performs 20-50% better than Python. This way, Anne was utterly taken by the charm of Go.

3. Minimalistic & Readable

Go follows the minimalistic approach for its AI and ML algorithms. After the implementation, developers of Go create very readable code because of this minimalistic approach.

You may also like to read: Why Golang? Settling the Debate Once and For All

Go vs Python for Machine Learning

Python vs Go Popularity

Key Takeaway: Python vs Go

Henceforth, both languages have their pros and cons. Anne hopes that this comparison of Python vs Go will be helpful to you while developing your Machine Learning Solution. If you are looking for assistance for Golang and wondering what other possibilities could be discovered for machine learning, then get in touch with the best Golang development company to build the next-gen-enterprise application.

Frequently Asked Questions (FAQs)

Here are some free resources/courses where you can learn ML with Python:

  • Google’s Python Class
  • MatPlotLib with Python
  • Introduction to Data Science using Python

Simplicity of Consistency, Exclusive Libraries, and Established Community are the key considerations for choosing Python for ML. And opt for it when you need to develop a flexible and platform-independent AI/ML project in budget-friendly pockets.

You should choose Golang for ML because it has exclusive AI Libraries, Exceptional Computation Speed, and it is Minimalistic & Readable. Use Go when you want to develop a flexible, versatile, robust, and state-of-the-art ML project.

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