An easy-to-follow guide to containerizing multi-service ML applications with Docker — Given Docker’s impressive capabilities of building, shipping, and running machine learning (ML) applications reliably, it is no surprise that its adoption has exploded and continues to surge within the data science field. This article explains how to utilize Docker to containerize a multi-service ML application built with H2O AutoML, MLflow…

Final insights (with lecture notes) from the Machine Learning Engineering for Production (MLOps) Course by DeepLearning.AI & Andrew Ng — Realizing the potential of machine learning (ML) in the real world goes beyond model training. By leveraging the best practices of MLOps, teams can better operationalize and manage the end-to-end lifecycles of ML models in a sustainable manner. In this final article of the 4-part MLOps Specialization series, I summarize…

Developing and managing a data science pipeline for detecting fraudulent credit card transactions — Data science promises to generate immense business value across all industries through the compelling capabilities of machine learning. However, a recent report by Gartner revealed that most data science projects fail to progress beyond experimentation despite having sufficient data and intent. To unlock the full potential of data science, machine…

Concepts and Python implementation of the regression technique based on principal component analysis (PCA) — Principal component analysis (PCA) is a well-known dimensionality reduction technique, but did you know that we can also apply the concepts behind PCA in regression analysis? This article provides a clear explanation of principal component regression (PCR), including its theoretical concept, benefits, caveats, and Python implementation.

A highly efficient approach to attaining the Amazon Web Services (AWS) machine learning specialty certification — I recently took and cleared the AWS Certified Machine Learning Specialty (MLS-C01) exam to validate my expertise in machine learning and competence in implementing it on the cloud. While many online learning paths are available, most are overly lengthy and laden with excessive options and resources. …

An easy-to-follow comprehensive guide on using Amazon RDS, MySQL Workbench, and PyMySQL to build and deploy a database in the cloud for Python applications — We will often generate and collect valuable data from the Python applications we build in our data science work or personal projects. It is thus vital to have an accompanying database that is scalable and performant so that the data can be effectively stored, organized, and queried.

Main insights (with lecture notes) from the Machine Learning Engineering for Production (MLOps) Course by DeepLearning.AI & Andrew Ng — Although machine learning (ML) and deep learning concepts are essential, possessing production engineering skills is equally (if not more) vital in solving real-world problems with data science. DeepLearning.AI developed the MLOps Specialization course to share practical lessons on building and maintaining ML systems in production.

Learn about advanced Google Search features to boost your efficiency in finding data science solutions — Of the many skills that a seasoned data practitioner should possess, the ability to Google search for answers accurately and efficiently is right there amongst the top. In this article, discover the top Google search tips that will help you search for data science solutions and ideas better and faster.

Exploring the definitions of essential technological concepts ranging from Altcoin to Cryptocurrency to Web 3.0 — The next wave of technological change involving the Metaverse and Web 3.0 has the potential to revolutionize the way we work, play and interact. These changes are bound to bring about new (and potentially confusing) terms and concepts. Here is a quick and easy-to-understand reference to the numerous definitions for…