The number of ML practitioners using python is much higher than GO developers. But the question arises why we are using Python to build the ML models? For a lot many developers the obvious answer will be because everyone else uses it. (😐)Here are some points from my side.
Why we use python for ML
- Great Community support
- Readability of the code makes it easy to reproduce
- Amazing library ecosystem
- Flexible with other languages
- Beginner level language (most importantly)
If you are a beginner in ML and starting hands-on with some ML libraries and exploring different applications with it then Go is not the best option for you. Go will make things complex for you and
may work as ML repellent to you. But if you master this skill then there’s no looking back.
- Go is surely faster (which is the reason to give it a try in AI) but because the math is written in a more complicated way, you’d need to know Data science and data structure. In Python, on the other hand, math is already written in C! which makes it so much easier.
- Fast build time as compared to python
- Good performance at run-time
Some of the worlds most successful technology companies use GO as the main language of their production systems and actively contribute to its development, such as Google, Uber, Dailymotion, Medium. This means that there is now an extensive ecosystem of tools and libraries to help a development team create a reliable, maintainable application in Go.
For all the Devs who are running their models on the cloud, Docker is also built using GO.
Python seems to be more focused towards web application development language whereas GO is designed to be a system language. If you want to allocate some memory to a specific operation and play with memory allocation for your applications. Then GO will give you that option.
Python keeps striking the list of the most demanded languages for AI programming. However, Golang is expanding its territory gradually. So far, Go has served great for web apps. Now it also has good potential for AI programming. Clean codebase, reused algorithms, and good scalability makes Golang a great technology for AI.
It is possible to fully integrate data science products into a Go application without sacrificing accuracy, speed, or forcing a data scientist to work with tools they are uncomfortable with.
So if you are good at building the models on your own and having a
good knowledge of maths and its algorithms then you must try GO for your next project.