Preferred learners
- Anywhere
- Academic experience or paid work
Categories
Project scope
- What is the main goal for this project?
-
We are an early stage Ed-Tech startup with partnerships from AWS, Stripe, and the Vector Institute that aims to build a platform where students and tutors are matched using our recommendation engine.
We would like to work with students to develop and refine our recommendation system that optimally matches students with tutors based on inputs from our intake questionnaire.
This will involve several different steps for the students, including:
- Analyzing our existing datasets of users, comprised of both students and tutors.
- Developing a recommendation engine software.
- Optimizing software runtime performance and assessing areas for improvement.
- Leveraging AWS to deploy matching models and host our platform.
- Testing the developed software by working closely with customers and making improvements based on additional data.
- What tasks will learners need to complete to achieve the project goal?
-
By the end of the project, students should demonstrate:
- Understanding of our existing dataset of users, products, and feedback.
- Solid understanding of fully-functional matching model architecture.
- Identification of areas for future improvement of the software.
Bonus steps would include:
- Testing the developed software with users and accounting for current state of business objectives.
Final deliverables should include:
- All source code.
- A written report explaining the design process and outcomes.
- What is the main goal for this project?
-
We are an early stage Ed-Tech startup with partnerships from AWS, Stripe, and the Vector Institute that aims to build a platform where students and tutors are matched using our recommendation engine.
We would like to work with students to develop and refine our recommendation system that optimally matches students with tutors based on inputs from our intake questionnaire.
This will involve several different steps for the students, including:
- Analyzing our existing datasets of users, comprised of both students and tutors.
- Developing a recommendation engine software.
- Optimizing software runtime performance and assessing areas for improvement.
- Leveraging AWS to deploy matching models and host our platform.
- Testing the developed software by working closely with customers and making improvements based on additional data.
- What tasks will learners need to complete to achieve the project goal?
-
By the end of the project, students should demonstrate:
- Understanding of our existing dataset of users, products, and feedback.
- Solid understanding of fully-functional matching model architecture.
- Identification of areas for future improvement of the software.
Bonus steps would include:
- Testing the developed software with users and accounting for current state of business objectives.
Final deliverables should include:
- All source code.
- A written report explaining the design process and outcomes.
- How will you support learners in completing the project?
-
Students will connect directly with the employer for mentorship and supervision throughout the project. Due to a small founding team size, communication amongst different departments will be common.
- What skills or technologies will help learners to complete the project?
-
In order to complete this project, students can self-teach, but it is beneficial to be familiar with:
- Recommendation engine development and data analysis skills
- Machine learning languages like Python along with knowledge of tools/packages like Tensorflow, PyTorch
- Experience and/or knowledge with LLM's and Prompt Engineering
Again, students can complete this project without any of the prior knowledge listed here. Students will be expected to research and learn more about the above as the project goes along.
Supported causes
About the company
- https://tutorai.ca
- 2 - 10 employees
- Education, It & computing
Simplifying learning, advancing education.
Tutor.AI is an AWS-backed and Vector Institute FastLane startup that aims to improve the tedious student-tutor matching process by matching compatible student and tutors via our AI-powered platform.