About this Event
Great leaps are being made in the development of Machine Learning (Artificial Intelligence) models that are capable of reading, identifying, and interpreting patterns in data; be they images, videos, words, or numbers. The systems that achieve this are complex by nature, and so are the teams that build them; typically comprising the blended skills of Data Engineers, Mathematicians, and Data Scientists. Together these people are tasked with developing systems that consume data, process it and generate valuable insights that are then presented to their human operators to assist them in their work.
By definition, these projects are complex and often experimental by nature, which means that Agile Practices should be ideally suited. But which ones to use and why? Why do Data Scientists, sometimes, rebel against being Agile? What do we need to do differently when working with experimental Data Science models, as opposed to established ones? Where does model training fit in? And how do we estimate tasks that are both simple and time-consuming?
In this session we will look at the practicalities of applying Agile Thinking and Frameworks to projects led by Data Science
Guest Speaker: Chris Wolf
Originally he trained as a developer but quickly found his place as a Technical Project Manager. He spent his first 12 years working for various Digital Agencies across London, delivering digital products and websites (as well as a few videos and bits of print) for global and international clients such as Disney, Land Rover, British Airway, and the British Government. 8 years ago, a chance conversation led him to discover Scrum, which in turn led to Agile.
The concepts and mindset of Scrum and Agile immediately resonated with Chris, and so began a new phase in his career. Accelerate 8 years, and Chris has assisted with the Agile Transformation of several Digital Agencies and supported Agile teams delivering work for Square Enix, Starbucks, Stella Atois, Smirnoff, and Unilever, amongst others.
Chris has spent the past 3 years working with Data Scientists & Machine Learning Engineers, within Shell R&D, to develop bleeding-edge solutions to help the businesses and energy suppliers of tomorrow.
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