Trellis Data Group: Data Migration and ML Model Training APIs Integration

6 May 2024

This position is offered through the ANU Computing Internship ([COMP3820] /[ COMP4820] / [COMP8830])

**Company**\

Trellis Data Group

Business Unit/Division: ML Research Labs

Trellis Data Group provides cutting-edge AI solutions deployed securely at the enterprise-level, specialising in knowledge mastery, transcription, translation, computer vision, and high-security deployment.

**Project **\

Background:

The objective of this project is to develop a set of APIs that facilitate the seamless movement of data from a service called DatasetManager to another service named TIP. Additionally, these APIs will serve as a bridge for training a Machine Learning (ML) model using the migrated data and producing inferences. The successful completion of this project requires proficiency in Elixir, REST API development, Python, and Torch Dataloaders.

Project Components:

API Development for Data Migration:
Create RESTful APIs using Elixir to extract data from DatasetManager.
Transform and format the data as required for compatibility with TIP.
Implement endpoints for securely transferring the formatted data to TIP.

API Integration with TIP:
Develop RESTful APIs in Elixir for TIP to receive the migrated data.
Ensure seamless integration with TIP’s data handling capabilities.
Implement authentication and authorization mechanisms to ensure data security during transfer.

ML Model Training APIs:
Design RESTful APIs in Python for initiating ML model training.
Utilize Torch Dataloaders to efficiently load and preprocess the migrated data.
Implement endpoints for configuring model parameters, such as architecture, hyperparameters, and optimization algorithms.

Inference Visualisation:
Implement a basic UI to visualise the model generated 3d scene output.

Project Deliverables:

Complete set of APIs developed in Elixir for data migration from DatasetManager to TIP.
Integrated APIs for seamless data transfer between DatasetManager and TIP.
RESTful APIs in Python for initiating ML model training and generating inferences.
Documentation detailing API endpoints, data formats, authentication mechanisms, and usage instructions.
Test suites to validate the functionality and reliability of the developed APIs.

Timeline:

The project is estimated to be completed within one semester, subject to review and adjustments based on the complexity of individual components and any unforeseen challenges encountered during development.

**Required technical skills**\

Proficiency in Elixir for backend API development.
Experience in developing RESTful APIs.
Proficiency with Javascript, React for front-end development.
Strong command of Python for ML model training and inference generation.
Familiarity with Torch Dataloaders for efficient data handling in PyTorch.
Understanding of data formats and compatibility between different tools.
Ability to implement secure authentication and authorisation mechanisms for data transfer.
Skills in error handling and debugging to ensure robustness of the developed APIs.

** Special Requirements **\

Police check required.

**Required professional/other skills**\

Professional communication (specific and accurate communication of thoughts)

**Delivery Mode**\

In-person internship.

**Location**
Canberra, ACT.

**Type of internship**\

Paid placement.

**How to apply**\

Applications are invited from students who have already passed the eligibility checks for the Computing Internship courses COMP3820 or COMP4820 or COMP8830. Further information about the Computing Internships can be found on the Computing Internship page.

You can nominate multiple preferred Internship projects and host organisations through the one application form.

The closing date for Expressions of Interest for internship projects is 19th May, 2024. Students who have passed the eligibility checks would have received the application form.

You are on Aboriginal land.

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

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