Leveraging Flask to Serve a Machine Learning Model

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Photo by Lefteris kallergis on Unsplash

For those that aren’t familiar with my writings, in a past post titled “Using Machine Learning To Detect Fraud” we started building the first parts of our Machine Learning package.

Now, the package is complete but what do we do next? How do we integrate this to wherever we need to use it — Package Repository on Github.

I hereby introduce you to the REST API.

Note: This project is highly inspired by the “Deploying Machine Learning Models” course on Udemy, therefore there will be snippets of source code taken from that project.

What is a REST API?

API is an acronym that is short for Application Programming Interface. Essentially, it is a software mediator that allows for two applications to talk to one another. If you aren’t familiar with API’s then you wouldn’t know you more than likely use one every day. Don’t believe me? When was the last time you sent an instant text message? …

Natural Language Processing Notes

Notes From Natural Language Processing Specialization Course 1 Week 1

Off the back of my last two posts, I thought it necessary we begin a new path. Together, we will walk through fundamental concepts in Natural Language Processing to serve as a kick start for the newcomers, and a reminder for the long-practitioners time that decides to read — starting with Sentiment Analysis.

Note: The post in this series will be created from notes I have taken off the Natural Language Processing Specialization on Coursera with extra things I’ve added because I thought it was useful.

Previous 2 posts:

In the examples given in our notes, we are intending on fitting a Logistic Regression model on to our features. I will not going much into the inner workings of Logistic Regression, however if you are extremely interested you may read “Algorithms from Scratch: Logisitc Regression”. …


Becoming a Specialist Will Take You Further than A Generalist

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Photo by Bermix Studio on Unsplash

In my last post, “The importance of Branding in Data Science” I mentioned that Data Science has become too broad hence branding is an excellent tactic that may be employed to overcome the noise of being a “Data Scientist” which in turn works in our favour during the vetting process.

After pondering on my own writing for some time, it made me wonder…

Would it of Been Easier If I just told people to specialize?

Despite the identity crisis we are facing as a community, I am usually not one to care about titles. However, I understand the importance of distinguishing between roles, and on that basis, I wouldn’t be surprised if we begin to see roles like “Statistical Modeller”, “Natural Language Processing Engineer”, or “Computer Vision Engineer” — Maybe not these exact names, but you get my gist — popping up, whilst roles like Data Analyst get to reclaim their identity. …


Kurtis Pykes

Passionate about fuelling the growth of Indespensible “Data Scientists”. https://www.linkedin.com/in/kurtispykes/

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