• A Comparative Analysis of Amazon SageMaker and Google Datalab
    Google Datalab and Amazon SageMaker have fully managed cloud Jupyter notebooks for designing and developing machine learning and deep learning models by leveraging serverless cloud engines. However, they exist key differences between the two offerings as much as they have a lot in common. This post carries out a comparative analysis to examine the subtle differences and similarities between the two cloud-based machine learning as-a-service platforms.
  • Exploring Amazon SageMaker
    Amazon SageMaker is another cloud-based fully managed data analytics/ machine learning modeling platform for designing, building and deploying data models. The key selling point of Amazon SageMaker is "zero-setup". This post takes a tour through spinning up a SageMaker notebook instance for data analytics/ modeling learning models.
  • Supervised Machine Learning: A Conversational Guide for Executives and Practitioners

    This post gives a systematic overview of the vital points to consider when building supervised learning models. We address in Q&A style some of the key decisions/issues to go over when building a machine learning/ deep learning model. Whether you are an executive or a machine learning engineer, you can use this article to help start comfortable conversations with each other to facilitate stronger communication about machine learning.

  • Demystifying Deep Learning
    Learning is a non-trivial task. How we learn deep representations as humans are high up there as one of the great enigmas of the world. What we consider trivial and to some others natural is a complex web of fine-grained and intricate processes that indeed have set us apart as unique creations in the universe both seen and unseen. In this post, I explain in simple terms the origins and promise of deep learning.
  • Understanding Machine Learning: An Executive Overview
    Machine learning is a technology that has grown to prominence over the past ten years (as at this time of writing) and is fast paving the way for the “Age of Automation”. This post provides a holistic view of the vital constituents that characterizes machine learning. At the end of this piece, the reader can be able to grasp the major landmarks and foundation stones of the field. Also, this overview provides a structured framework to wade deeper into murkier waters without getting overly overwhelmed.
  • A Gentle Introduction to Google Cloud Platform for Machine Learning Practice
    In a previous post titled Machine Learning on the cloud, we examined in plain language what is machine learning, what is the cloud, and the merits of leveraging cloud resources for machine learning practice. In this post, I introduce GCP as a simple, yet powerful, and cost effective cloud option for performing machine learning. Whats more, I provide a simple walkthrough on how to set up the environment for machine learning model development on GCP.
  • Machine Learning on the Cloud: Notes for the Layman
    Computational expenses have always been the bane of large-scale machine learning. In this post, I explain the fundamentals of Machine Learning on the cloud and the opportunities of unbridled computational horsepower made available by leveraging cloud infrastructures.