Today I want to share with you my experience on deploying Libraries on AWS Lambda. And the best practices I found.
This experience is shown with Python but can be replicated with other programming languages.
They are multiples ways to deploy packaged libraries on AWS Lambda. The easiest would be to zip your project and to upload it directly on Lambda if the package is smaller than 50 MB and using AWS S3 if the package is bigger than 50 MB. Note that the package has to be smaller than 250 MB once unzipped anyway.
Nowadays, every data scientist should know how to integrate their models within a cloud platform so that they can enhance their work and become more valuable as a data scientist. Unfortunately integration concept is a bit hard when you are beginner but luckily this story is therefore for you if you want to build your first machine learning pipeline on the cloud and more precisely on Amazon Web Services (AWS).
As you can see on the schema, the pipeline’s input is a S3 upload of some data and the pipeline’s output is the data preprocessed written on S3. …
Natural language processing is a recurrent topic about machine learning, and there are many ways to deal with it. In this topic I will focus on the discriminant power analysis, a very interesting data featuring method for binary classification.
This method consists in finding the most discriminant words between two classes of target. This morphological approach is interesting as, despite a low complexity, it gives good results.
For this article I will detail a full example from preprocessing to modelling and prediction with the spam data set available on kaggle. Note that in this article, although I will give you…