03 Xin Liang, Day 1, DataEngBytes 2021

From experiment to production - a journey of a machine learning model Xin Liang Xin is the lead machine learning engineer at Eliiza, who has considerable experience in both software engineering and machine learning. She has worked with technologies such as deep learning, computer vision, natural language processing, signal processing, web and product development, and cloud engineering. With her great passion for Artificial Intelligence, she is focusing on using her engineering skills to develop, build, productionise and scale machine learning solutions. Machine learning has a wider and wider adoption in the industry as a technology to build intelligent and data enriched systems. However, the reality is that machine learning practice often stops at the end of rapid experiments before its value can be harvested in the real world. This is because the process and the culture of taking ML models from experiment to production is lacking. A machine learning project typically consists of experiment, development and deployment three main phrases. Correspondingly, a machine learning system consists of processes and components that facilitate the operations and data flows in those three main phrases. This system provides a framework and best practices for machine learning practitioners to develop models from experiment to production. This talk explores the process pattern of getting a machine learning model into production and the system to support this operation. An example of productionising a natural language processing model will be used to illustrate how a model travels through experiment tracking, data artifacts management, data / machine learning pipelines and goes into production. The talk will also provide examples of tooling and patterns to be used at each stage.