Data Insights
Insights from our consultants covering artificial intelligence (AI), machine learning, data engineering, strategy, and governance.
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Machine Learning Models Debugging & Testing (1/2)
Testing and debugging machine learning (ML) systems differs significantly from testing and debugging traditional software….
Data Quality Management for Machine Learning
Data is the fuel for our future and data is the lifeblood of an organization….
Data Governance for Advanced Analytics, Machine Learning & Automation
Data Governance seeks to bring order to the chaos that emerges as organisations turn to…
Online Learning: The Challenging Data Frontier
Introduction Online learning is a subfield of machine learning where practitioners sometimes refer to as…
Data Engineering with SQL Server Integration Services (SSIS)
Introduction SSIS (SQL Server Integration Services) is an upgrade of DTS (Data Transformation Services), which…
Automated AI approaches to clinical coding “A Case Study”
Introduction Clinical coding is an administrative process that involves the translation of the diagnostic data from…
Working with Firebase
First impressions on using Firebase for a complete end-to-end solution
Getting started with Microsoft PowerApps
What is PowerApps? PowerApps is part of Microsoft Power Platform (Fig 1.0) along with Power…
Easily Adding Smart Features to Your App with Azure Cognitive Services
In the early and mid 2000, AI is something mythical that only tech giants could…
A guided introduction to Exploratory Data Analysis (EDA) using Python
Exploratory Data Analysis (EDA) is the most critical initial step for Data Scientists to analyze…
MLOps: CI/CD for Machine Learning Pipelines & Model Deployment with Kubeflow
Open-Source Continuous Delivery for Machine Learning models using Kubeflow and Kubernetes with source code
Top test automation challenges and how to solve them
By using automated testing, we can expedite the process of software validation, scale and accelerate tests, reduce errors, and buy back time on software projects. While test automation has clear benefits, in many cases automation efforts fail because they lack proper planning and preparation up front. This topic aims to outline the top challenges that have the highest impact on the overall automation test effort and project success
Kubeflow Pipelines – Running and Deploying Pipelines via the SDK
This document builds on our getting started guide and takes you through the process to…
Getting started with Kubeflow v0.6 on GCP (via the CLI)
Kubeflow has matured dramatically with its last two releases, not only becoming much more stable…
Time-series decomposition of Daily Defence Spending data using stR
Time series decomposition involves thinking of a series as a combination of level, trend, seasonality, and…
The “Full Stack” Data Scientist
The term “Full Stack” has been in common usage for the last 10 or so…
Big Data: Correlation is no substitute for causation (or a decent model)
I have been working with “Big Data” for the past seven or so years, working…
Building fast succinct data structures in C
This is being discussed over at Hacker News, check it out. In developing software for…
Why are column oriented databases so much faster than row oriented databases?
I have been playing around with Hybrid Word Aligned Bitmaps for a few weeks now,…