Luke Menzies
Advancing Analytics, Principal AI Consultant, Dr
Stockport, United Kingdom
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I am a data scientist at Advancing Analytics that has been in the field for over four years. Before entering the data science world, I completed a PhD in computational materials modelling for applications in nuclear fusion. I have always taken an interest in using the language of mathematics to deliver tangible solutions to the world around me.
Area of Expertise
Topics
Introduction to Building Scalable Machine Learning Models
Machine learning is a forever-evolving space. In the early days, the focus was on implementing the historic algorithms using the computing power that was previously unavailable. Currently, ML has expanded across multiple avenues of interest: Being able to scale models with the ever-increasing quantities of data, the ability to package models up so that they can fit into an MLOps framework, how well tracked and logged the models are, how explainable they are, not to mention techniques to squeeze the best performance out of the models. All these aspects are rarely covered all at once. This pre-con aims to take all these key topics and deliver them over the Databricks platform, one of the leading tools in the industry for data manipulation, machine learning and distributed computing. It utilises a powerful Spark engine under the hood whilst presenting an easy-to-use bespoke notebook for the front end.
This session calls upon experience gained over the years by experienced machine learning practitioners to deliver a condensed, concise course to help fast-track any machine learning novice towards mastery.
The pre-con will cover building real-world models in Azure, further techniques for robust model building, the latest craze in ML known as feature stores, advanced model tracking and key considerations when scoring models. The pre-con will contain talks, coupled with follow-along labs giving an element of hand-holding with some more tricky tasks thrown in. The sessions will also explore real-world use cases that are rife in the business world.
Optimisation in Business
An often overlooked application of data science in business involves prescriptive methods. These are the methods associated with making cars drive autonomously, dynamically adjusting routes to account for traffic, or automatically regulating system temperatures to deliver optimal operations. This field is often referred to by the catchier name for businesses as optimisation. The art of choosing the perfect set of actions to reach the ultimate value that can be achieved. This talk covers a range of use cases a business might not think to call upon the expertise of a data scientist for. This talk will also delve into the mechanics of the techniques used to deliver prescribed choices.
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