Setup Kubernetes For Local Development Via Minikube — Dread it, Run from it, Kubernetes arrives all the same. Familiarity with the most popular container orchestration tool is unavoidable in any modern day machine learning environment (or any software development environment for that matter). It is no secret that Kubernetes has a reputation of being a tool most developers…

How To Build A Neural Network Model With No Machine Learning library — One of the popular requests that I usually get from readers who love the “building algorithms from scratch” series is coverage on deep learning or neural networks. Neural network models can be quite complex but at their core, most architectures have a common base from which new logics have emerged…

An Exciting Platform For Historical & Real-Time Financial Data — You may have heard this quote “Data Is The New Oil” one too many times. Regardless of one’s opinion on this assertion, it is widely recognised that data is crucial to the innovative processes of every industry. The financial industry, in particular, is one industry where having access to quality…

How To Build From Scratch Regularized (L2) Logistic Regression in Julia — Amazing libraries for machine learning tasks (such as scikit-learn) have made it ridiculously easy to generate machine learning models these days. Gone are the days when one had to write every model from scratch. It’s a good thing that we have moved past those times as most problems end up…

Building Multivariate Linear Regression With Gradient Descent From Scratch In Julia — Currently, Python and R are undoubtedly the most widely used programming languages in the machine learning world. Unfortunately, the high-level and elegant abstractions from these programming usually come at a cost, especially when working with large systems. Before we even begin, this post will not be some endless bashing of…

Generating deep learning model is highly experimental by nature and design. This experimental feature is also one of the biggest pains of trying to generate deep learning models. It can be time-consuming as well as computationally expensive as one fidgets a lot with lots of tweaks in a bid to…

Machine learning models are meant to be deployed! For some reason, coverage on the deployment of machine learning models is very thin in both literature and the blogging space. It seems the glamour of designing the shiniest and latest cutting edge model with state of the art results appeals more…

In their paper in 2016, Kumar et al. advanced for machine learning practitioners in their paper titled “Model Selection Management Systems: The Next Frontier of Advanced Analytics” a 3-staged process for the selection of machine learning models. They named this process the “The Model Selection Triple”. The selection and implementation…