Cancelled: Bridging the gap between research and production for machine learning
- Date: Wed, 1 Apr 2020, 5:30 pm - 6:30 pm
- Location: Horace Lamb Lecture Theatre, North Terrace Campus
- Cost: Free but registration essential
- Contact: Marian MacLucas marian.maclucas@adelaide.edu.au
The 2020 Dean's Research Seminar Series kicks off with guest speaker and best-selling author Chip Huyen who is working with a US based start-up that focuses on the machine learning production pipeline.
Humanity has come a long way from the days we were looking up to the stars to “predict” the future to predicting human behavior with machine learning. However, embracing AI and machine learning is just the first step for modern businesses. Chip Huyen
In this presentation, Chip will explore the key differences between the research and production environments:
- data
- goals
- compute requirements
- evaluation metrics
These differences - together with the lack of communication between researchers and product engineers, the misalignment of business performance with technical performance, an undeveloped infrastructure, and the lack of a feedback loop - make it challenging for organizations to productionize promising research.
Bridging the gap between research and production for machine learning
Date: Wednesday 1 April
Time: 5.30pm - 6.30pm followed by light refreshments
Location: Horace Lamb Lecture Theatre
About the speaker
Chip Huyen works to bring the best engineering practices to machine learning research and production. Her experiences include Netflix, NVIDIA, and Primer. Previously, she created and taught the Stanford course TensorFlow for Deep Learning Research. She’s currently with a startup that focuses on the machine learning production pipeline.
When not teaching machines or helping companies set up their in-house machine learning teams, she writes. She’s the author of four bestselling Vietnamese books, and her writings have been published in leading newspapers in the USA, France, and Vietnam. She’s currently working on a book on Machine Learning Interviews.