Enhancing DAG Deployment and Testing through User-Centered Design

Geotab, the global leader in connected vehicle and asset solutions, leverages advanced data analytics and AI to enhance fleet performance, safety, and sustainability while optimizing costs. Backed by a team of industry leading data scientists, engineers, and AI experts, we serve over 50,000 customers
across 160 countries, processing billions of data points hourly from more than 4 million vehicles.
The Geotab Data Platform team is responsible for enabling and empowering the work of data scientists and developers by providing a platform for data ingestion, orchestration, digestion, and all applications of data. The sheer quantity of data that flows Geotab requires significant engineering hours working towards deployments, environment configuration, error handling, anomaly detection, and workflow performance optimizations. The goal of this project is to explore and develop automation tooling leveraging AI to increase efficiency, reduce manual effort, reduce errors, and identify ways to improve performance within the system.
Improvements to all the above makes impacts throughout Geotab, improving the quality of the solutions provided, as well as increasing the throughput of data, allowing Geotab to serve more customers and bring in more revenue.

Faculty Supervisor:

Azadeh Farzan

Student:

Partner:

Geotab Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services; Transportation and warehousing

University:

University of Toronto

Program: