Austin Goh
About Austin Goh
Austin Goh is a Senior Manager of Data Science at AirAsia, where he leads the development of the Cortex AI Platform, enabling users to perform data processing and machine learning without coding. He has a background in Artificial Intelligence and Web Technology, and he implements microservice architecture and DevOps practices in his work.
Work at AirAsia
Austin Goh has been serving as the Senior Manager of Data Science at AirAsia since 2018. In this role, he leads a team of engineers in an Agile environment to develop the Cortex AI Platform. This platform enables users to perform data processing and machine learning without the need for coding. His responsibilities include overseeing the implementation of microservice architecture and ensuring the platform's functionality aligns with business objectives.
Education and Expertise
Austin Goh studied at the University of Malaya, where he earned a Bachelor of Computer Science with a focus on Artificial Intelligence from 2013 to 2017. He furthered his education at the Korea Advanced Institute of Science and Technology, participating in an Exchange Program in Web Technology from 2015 to 2016. His academic background provides a solid foundation for his expertise in data science and software development.
Technical Skills and Contributions
Austin has implemented various technical practices in his role, including DevOps methodologies such as Linux Server Administration and deployment using Nginx, Docker, and Gunicorn. He has developed the Social Media Analysis Module utilizing Python, NLTK, polyglot, and PyTorch. Additionally, he designed a message queue system using RabbitMQ for real-time applications and established a Hadoop File Server for client file storage.
System Architecture and Database Management
In his capacity as a System Architect, Austin Goh has implemented microservice architecture for the Cortex AI Platform. He administers database design using CassandraDB and has set up Nexus Sonatype for private repositories. His work ensures that the platform is scalable and efficient, supporting the needs of users in data processing and machine learning.