Introduction to Machine Learning Engineering
Wednesday, February 27, 2019 - 6:00 PM to 8:00 PM - Traxonomy
What is machine learning engineering? It’s a new field that combines the science and methods of machine learning with traditional software and systems engineering. In this talk, Garrett Smith, founder of Guild AI, will present the challenges and opportunities for practitioners who want to apply machine learning to build exciting new applications. Garrett will cover the motivation for using machine learning in applications, tools of the trade, data analysis, model selection, model training, infrastructure, reproducibility, and production. All of these topics roll into complex, deep, and fascinating areas of work. If you’re a software programmer or engineer looking to get into machine learning, you’ll learn what’s involved in expanding your skillset into the realm of machine learning. If you’re a machine learning scientist or researcher, you’ll learn about the engineering processes needed to support your work in building applications.
Garrett Smith is founder of Guild AI, an open source toolkit for running, tracking, and comparing machine learning experiments. He is also co-organizer of Chicago ML and TensorFlow Chicago. He has over 20 years experience building software and systems. Prior to his work on Guild AI, Garrett built and ran CloudBees application platform, which hosted tens of thousands of enterprise applications at scale.
We will also host short presentations by members of the community on projects they’re working on:
- Aman Agarwal - Use of Transfer Learning in Text Classification using FastAI library. Till now, transfer learning was mainly used in computer vision but FastAI is a wrapper library built around PyTorch that has brought this state-of-the-art approach in NLP tasks. I will be demonstrating the use of this library for US-Airline Sentiment Analysis.
- Jay Rodge - Classifying Audio-MNIST using PyTorch.
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