Productive reproducible workflows for deep learning-enabled large-scale industry systems

Overview

Deep learning is a mainstream to increase the capability of industry systems, particularly for those with massive data input and output. It is seen that many tools are now claimed to be freely available and could facilitate such process of development and deployment significantly with scalability and quality.

However, limited attention has been on developing reproducible and productive workflows to identify the tools and their values towards large-scale industry systems. In this project, we will explore how to design such a workflow to increase the productivity for both deep learning engineers and researchers.

Research activities

In this project, you will conduct research on improving deep learning-enabled large-scale industry systems, particularly exploring a productive reproducible workflow for such systems development and deployment. In details, the project will:

• identify core tools and existing workflows to facilitate the development of deep learning-enabled large-scale industry systems
• understand the pitfalls and impacts of leveraging such tools and workflows.

You will be expected to work with Dr. Yi Lu (QUT) and Dr. Huaming Chen (USYD) for this project.

Outcomes

Given the specific goals for this project, you are expected to:

• build productive reproducible workflow for deep learning-enabled large-scale industry systems
• benchmark the workflow for performance evaluation.

Skills and experience

To be considered for this project, you are expected to have:

• knowledge of machine learning.
• knowledge or experience of software development and deployment
• good programming skills.

Scholarships

You may be eligible to apply for a research scholarship.

Explore our research scholarships

Keywords

• machine learning
• software engineering

Contact

Contact the supervisor for more information.