What is Model Drift

This quote was by a Greek philosopher named Heraclitus, and it’s such an interesting quote because it’s unironically true. The term ‘constant’is defined as occurring continuously over a period of time, and so, you could say that change is perpetual. This poses a problem for machine learning models, as a model is optimized based on the variables and parameters in the time that it was created. A common and sometimes incorrect assumption made while developing a machine learning model is that each data point is an independent and identically distributed (i.i.d) random variable. Imagine a classification model created to detect phishy emails (spam emails) created… Read More

How to Deploy ML models

Deploying a machine learning model is one of the most important parts of an ML pipeline and really determines the applicability and accessibility of the model. Building a machine learning model is one of the most challenging tasks of building a ML pipeline for processing and predicting data but deploying it successfully is critical in order to convert your time and effort into real output. There are several important aspects of model deployment that need to be considered while thinking about deploying ML models.    Data access and query: You need to make sure that your model would have easy… Read More

The Naive Bayes Classifier

The new generation of people would probably never have to experience the stress of parsing through tons of emails, only to realise they are full of spam. Now, when you log into your email it’s highly likely that your email providers have implemented a form of filtering that automatically places all the spam emails into a separate spam folder. This phenomena is known as spam filtering, but there are other features, some being more subtle to the untrained eye such as text classification which involves assigning categories to unstructured text, or analysing the sentiment of messages from twitter.    Many… Read More