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Greg Ainslie-Malik

Greg Ainslie-Malik

Greg is a recovering mathematician and part of the technical advisory team at Splunk, specialising in how to get value from machine learning and advanced analytics. Previously the product manager for Splunk’s Machine Learning Toolkit (MLTK) he helped set the strategy for machine learning in the core Splunk platform. A particular career highlight was partnering with the World Economic Forum to provide subject matter expertise on the AI Procurement in a Box project.

Before working at Splunk he spent a number of years with Deloitte and prior to that BAE Systems Detica working as a data scientist. Ahead of getting a proper job he spent way too long at university collecting degrees in maths including a PhD on “Mathematical Analysis of PWM Processes”.

When he is not at work he is usually herding his three young lads around while thinking that work is significantly more relaxing than being at home…

Platform 2 Min Read

Smarter Root Cause Analysis: Determining Causality from your ITSI KPIs

Root cause analysis can be a difficult challenge when you are troubleshooting complex IT systems. In this blog, we are going to take you through how you can perform root cause analysis on your IT Service Intelligence (ITSI) episodes using machine learning, or more specifically causal inference.
Platform 6 Min Read

Smarter ITSI Episodes Powered by Community Detection Algorithms

In this blog we are going to describe how you can create a notable event policy in IT Service Intelligence (ITSI) that is able to group your events using labels generated by unsupervised machine learning in the Smart ITSI Insights App for Splunk – and don’t worry you don’t have to be a data scientist to read this blog!
Platform 3 Min Read

Making Smarter Predictions in ITSI

As we are trying to commoditize machine learning through our MLTK smart workflows, this article outlines another example of an MLTK smart workflow, designed to help improve the usability of the predictive capabilities in ITSI.
Platform 6 Min Read

Causal Inference: Determining Influence in Messy Data

In this blog, we’re going to look at how causal inference can be used to understand in more detail what the biggest influencing factors are across a dataset.
Platform 5 Min Read

Smart Education Insights App for Splunk – Part II

In this blog, we will describe how to apply feature engineering to some example datasets taken from higher education organisations, which can then be used to generate predictive models in the Smart Education Insights app.
Platform 5 Min Read

Smart Education Insights App for Splunk – Part I

This blog outlines how we have turned our #EUvsVirus project into reality with the new Smart Education Insights App for Splunk.