Jade Abbott is the Machine Learning Lead at Retro Rabbit in South Africa. She has an MSc Computer Science from the University of Pretoria and works as a software engineer across Africa in every field from fintech, to NGOs, to startups. Currently, she trains and deploys deep learning systems to perform a variety of tasks for real world systems. In 2019, she co-founded Masakhane, an open research grassroots natural language processing initiative for Africans, by Africans, which aims to spur research into NLP for African languages, currently boasting over 400 members, from 38 African countries, and 13 affiliated publications.
93% of data science projects never make it into production (MIT Sloan Management Review). Many of us in the field of AI feel the impact of this - the sheer futility of all our efforts.
First we're going to talk about how we ended up in this mess: Spoiler Alert: We were misled. Next, we're going to figure out how to get OUT of it.
It's time to put down those neural networks, and pick up some data governance tools. It's time to stop hiring data scientists and instead hire data engineers. Join me on a journey to go back and rework some of the foundations so that our Data Science efforts can begin to make impact.
Audience: Anyone who works in data science or runs a data science team
What will they get out of it:
- Increased Skepticism on what the tech zeitgeist proposes
- A list of many of the practices we follow in Data Science which are ill advised
- What we can change to ensure we actually make an impact