Start your hands-on journey with MLOps here — Introduction MLOps is a difficult set of concepts to learn at home. If you don’t have a production environment, it can be hard to simulate what happens in the real world. I’ve been investigating ways to develop MLOps at home and have selected some tools which implement the concepts and just…

A much overlooked way to save some time. — Introduction 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 file from a Jupyter Notebook, so I’ve…

Start by making your code reusable, then look at tooling — Introduction 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: Creating a Modern, Open Source MLOps Stack at Home towardsdatascience.com Since writing the above article, I have…

Data to support your search for the next meme stock. Introduction 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? — Introduction 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? — Introduction 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… — Introduction 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 What…

Part 1: Developing a Framework — Introduction 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 — Introduction 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…