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…

.conf & .conf Go 2 Min Read

Bring Intelligence to Your .conf21 Virtual Experience with Machine Learning

If you're looking to make your data smarter, look no further – with a new release of the Splunk Machine Learning Toolkit, several sessions at .conf21, a hands-on workshop and more, this blog has all the info you need to help bring intelligence to your everyday operations.

Platform 5 Min Read

Cyclical Statistical Forecasts and Anomalies – Part 5

When your datasets are far from simple, your anomaly detection techniques must evolve to scale with the growing complexity. In this blog, you will learn various ways to take your anomaly detection to the next level no matter the complexity of your data.

Observability 4 Min Read

Monitoring Model Drift in ITSI

In this blog we will talk about some strategies for monitoring your models in ITSI for model drift. This is the idea that the predictive models will become less accurate over time as the rules that were generated originally no longer match the data they are applied to.

Platform 4 Min Read

Exploratory Data Analysis for Anomaly Detection

With great choice comes great responsibility. One of the most frequent questions we encounter when speaking about anomaly detection is how do I choose the best approach for identifying anomalies in my data? The simplest answer to this question is one of the dark arts of data science: Exploratory Data Analysis (EDA).

Platform 2 Min Read

Levelling up your ITSI Deployment using Machine Learning

To help our customers extract the most value from their IT Service Intelligence (ITSI) deployments, Splunker Greg Ainslie-Malik created this blog series. Here he presents a number of techniques that have been used to get the most out of ITSI using machine learning.

Platform 8 Min Read

Smarter Noise Reduction in ITSI

How can you use statistical analysis to identify whether you have an unusual number of events, and how can similar techniques be applied to non-numeric data to see if descriptions and sourcetype combinations appear unusual? Read all about it in this blog.