A much overlooked way to save some time.


Want to jump straight to the best option? Click here.

Creating a requirements.txt file is a necessary process, particularly when sharing your code, developing MLOps or just pushing something up into a Docker container.

It is surprisingly tricky to get a clean requirements.txt …

Start by making your code reusable, then look at tooling


MLOps is one if the most popular buzzwords in Machine Learning and Data Science at the moment, but one of the areas least covered by online courses, YouTube videos and bootcamps.

You can read up on how I define MLOps at home here:

Since writing the above article, I have…

Data to support your search for the next meme stock.


Financial data is the backbone of modern Hedge Funds, Banks, FinTechs and many others. These organisations typically have plenty of cash and are able to spend it on data, hence financial data often fetches a hefty price tag.

As a…

Why use complex model when simple do trick?


As we know, Machine Learning is ubiquitous in our day to day lives. From product recommendations on Amazon, targeted advertising, and suggestions of what to watch, to funny Instagram filters.

If something goes wrong with these, it probably won’t ruin your life. …

TabNet balances explainability and model performance on tabular data, but can it dethrone boosted tree models?

TabNet model architecture. How does TabNet work?


Gradient Boosting models such as XGBoost, LightGBM and Catboost have long been considered best in class for tabular data. Even with rapid progress in NLP and Computer Vision, Neural Networks are still routinely surpassed by tree-based models on tabular data.

Enter Google’s TabNet in 2019. According to the paper, this…

And why you need to know about it…

Linear Model vs Generalised Additive Model


Linear Models are considered the Swiss Army Knife of models. There are many adaptations we can make to adapt the model to perform well on a variety of conditions and data types.

Generalised Additive Models (GAMs) are an adaptation that allows us to model non-linear data while maintaining explainability.

Table of Contents

Part 1: Developing a Framework


Over the past few years, many organisations have found that although they train great models, they don’t always gain long-term value from them. The reason for this is deployment and monitoring.

Deploying a model isn’t always that easy. Sometimes they are large. How long does inference take? You might need…

A framework for project success


Data Science is still a roaring field with demand continuing to outstrip supply and many business expecting to increase their IT spend drastically over the next few years.

Although there has been a sharp rise in online courses, bootcamps and degrees and with them, an increase in junior talent, it…

Deep learning doesn’t have to be complex


When I first started learning Data Science and looking at projects, I thought you could either do a Deep Learning or regular project. This is not the case.

With powerful models becoming more and more accessible, we can easily leverage some of the power of deep learning without having to…

What I’ve learned after writing 9 articles on Medium — 10 tips for my 10th article.


I’ve been an avid reader on Medium for well over a year now, but I only started writing towards the end of 2020.

While I haven’t enjoyed any considerable success so far, my stories…

Adam Shafi

Data Scientist | Get in touch: https://www.linkedin.com/in/adamshafi

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