AWS Lambda for machine learning engineers

( This tutorial covers the steps in the deployment of machine learning model in Lambda )

Deployment of the machine learning model is a tedious task but with services like AWS lambda, it does not have to be anymore. AWS lambda falls under the concept of serverless computing which means that we can deploy our model easily without the headache of managing servers, runtimes, and infrastructures. Lambda provides runtimes for programming languages like Python, Ruby and more.

Utilizing this benefit of serverless computing, we can easily deploy our machine learning models like classification model, regression model and much more at ease quickly and efficiently. We get to choose the processing power of our own choice and runtimes that apt requirements of our machine learning model.

Note: If you are new to linear regression or polynomial regression then I highly recommend you check out my linear regression for busy data scientists or polynomial regression in R tutorial.

One drawback of AWS lambda is that there is a limit in size of dependencies our model can have. For example libraries like spacy with the English language, the model can take up to a GB of RAM. But most of the models like classification models can easily be deployed since they have dependencies like SkLearn, NumPy or Pandas. And the good…