In today’s digital age, AI and machine learning systems play a crucial role in decision-making. Ensuring the security of these systems is non-negotiable. But how can we measure our security’s effectiveness? Enter pentests. A penetration test, or pentest, is a cyberattack simulation carried out by ethical hackers. It’s akin to…

Harnessing AI and ML decision advantage in offline and edge environments using Spiral Data’s IOT/AIML platform — AI Briefcase. In this extended overview, we outline the use cases and opportunities where a disconnected or edge device with AI ML capabilities will be of greatest benefit to an organisation. There is something inherently thrilling about what can take place within private networks and computing at the edge, which speaks to…

How we made our AI Briefcase a reality for disconnected, secure and edge scenarios. Spiral Data’s Chief Technical Officer, Chris Jansz, details the process to date. Spiral Data’s IoT to AI/ML platform has matured in the cloud through several years of feature development, user experience optimisation and processing complexity enhancements…

In my role as Chief Data Scientist for Spiral Data Group, we’re handling massive time series data sets ) which are used to identify, remediate and innovate within a defined use case. A perfect example is transient detection patterns across a water utility’s vast pipe network. …

How Spiral Data adapted its Machine Learning data labelling pipeline to reduce time spent labelling by over 90% (and increase client satisfaction!). You can read more about our AI/ML data platform here. We can draw a direct correlation between the number of data projects that Spiral Data has completed and…

As Spiral Data’s AI/ML platform matures, we explore the importance of the right tooling in order to execute well. In this instance, our data visualisation toolset. “We observed a browser struggle to render over 600,000 data points… Before it crashed. …

In today’s article, we will take you through the journey of our infrastructure deployment process where (as the title mentions) we went from 3 weeks to 3 days to 3 hours to deploy using AWS Service Catalog. …

This article focuses on providing an entry-level understanding of what and how acoustics (recorded sounds) can be used for machine learning to identify defects, improve efficiency and reduce cost. More and more businesses are relying on artificial intelligence and machine learning (AI/ML) for identifying defects and predicting failure points. This…

Recap The IoT to ML Reference Architecture helps reduce implementation time and cost and implements best practices. The modularised setup of the architecture allows us to customise solutions to best fit our customers’ requirements. The updates It’s been a while since we released v1.0 of the IoT to ML Reference Architecture and since…