Python is the most popular language for building machine learning projects. Although there are other languages like R, C++, and Julia, Python remains the most prominent and most used language in every machine learning framework. But to actually run a production machine learning API, we need features such as:

  1. Autoscaling
  2. API management
  3. Rolling updates

Go is an ideal match for these features, for a few reasons –

  • Concurrency – A user doesn’t really interact with an API directly, instead Cortez programming calls these APIs, launch and deploy them. Making all these overlapping API calls can be a challenge. Go has a solution to this problem – Goroutines. Goroutines are threads managed by the Go runtime. Numerous Goroutines can fit on a single OS thread. 
  • Cross-platform CLI – Originally, CLI was written in Phyton, but distributing it among different platforms was difficult. Go compiles down to a single binary. It offers a simple solution to distribute CLI across platforms without any extra work. 
  • Infrastructure projects – Open-source projects such as Kubectl, Minikube, Helm, Kops, and Eksctl are written in Go. This is attracting developers around the globe who are interested in working on infrastructure projects. 
  • Go is a pleasure to work with – Go might be a bit painful to get started with. But it is a joy to work with, especially for large projects. 

If you are interested in becoming a machine learning engineer, Python is your go-to programming language. But however, if you are interested in machine learning infrastructure, then you should definitely go for Go. Read on to know more about Go 

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