Technical Business Intern working within cross functional teams to commercialize AI-powered solutions in the Public Services Sector

AltaML builds artificial intelligence (AI)-enabled solutions to business problems. We work with organisations, bringing together their data and domain expertise with our AI expertise, to develop AI solutions that are deployed in their operations. We also commercialize AI-enabled products business via industry-specific ventures, yielding scalability from our investment in the first solution. AltaML’s AI Lab for Government, also known as GovLab, is a talent accelerator for public service professionals, post-secondary students and recent graduates. GovLab.ai’s mission is to set a global example of how to transform the public sector through applied AI, and is designed to encourage the growth of technical and business AI skill sets that are in high demand across Alberta and around the world. AltaML’s Venture Studio is an incubator program that works with founders and co-founders in the emerging tech industry to scale ideas, build venture products, and launch AI/ML startups across numerous industries; this includes our venture partners at Brilliant Harvest (AgTech), Jurisage Group Inc. (Legal Tech), and Prevoir (Fashion Tech).
The intern listed (Rachel Wang) on this particular application will continue to work alongside the Government of Alberta team to further develop our use case labelled “Document Classification” using Machine Learning. The goal is to enable the Information Management team in the Government of Alberta to automate the labeling of hundreds of millions of documents with a document classification taxonomy based on function and activity.
? Focus: Using Object Character Recognition, and Natural Language Processing techniques along with Large Language Models, this pilot will train ML models to recommend a classification label for each document. Additional deliverables include front-end development for bulk classification, architecture design for secure training based on privacy requirements, and efficient design for cost savings at scale.
? Value: The GoA stores an estimated 500-600 million documents, many of which are unclassified. The goal of this PoC was to explore classifying documents with machine learning so they may receive an appropriate retention schedule. A machine learning solution would ensure documents are classified, making them easier to find and mitigating productivity loss. The solution would also reduce the cost of storing documents that should have been disposed of.
The intern listed (Veronica Garth) on this particular application will work alongside the Government of Alberta Health team to further develop and pilot our use case labelled “Health Demand Forecasting” using Machine Learning. The goal is to enable the Alberta Health team to accurately forecast the demand for seasonal immunizations and healthcare resources, ensuring sufficient supply coverage while minimizing waste.
? Focus: Using time series analysis, unsupervised clustering, and supervised learning techniques, this pilot will train ML models to predict the number of immunizations required based on emerging trends, seasonal outbreaks, and population health indicators. Additional deliverables include developing forecasting dashboards, designing scalable architecture for secure data handling, and optimizing the models for real-time prediction and integration with supply chain systems.
? Value: The Alberta Health system faces fluctuating demand for immunizations and medical supplies due to seasonal and demographic factors. A machine learning solution would provide accurate forecasts to support better planning and resource allocation, reducing waste and improving public health outcomes. This approach would enable proactive decision-making, ensuring Albertans have timely access to immunizations while improving efficiency across the healthcare supply chain.

Faculty Supervisor:

Michael Maier

Student:

Partner:

AltaML

Discipline:

Business

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Alberta

Program: