Enhanced and automated MLOps systems to support the food and agriculture industry - ON-957

Project type: Research
Desired discipline(s): Engineering - computer / electrical, Engineering, Computer science, Mathematical Sciences
Company: Anonymous
Project Length: 4 to 6 months
Preferred start date: As soon as possible.
Language requirement: English
Location(s): Stoney Creek, ON, Canada; Halifax, NS, Canada; Toronto, ON, Canada
No. of positions: 1
Desired education level: Postdoctoral fellowRecent graduate
Open to applicants registered at an institution outside of Canada: Yes

About the company: 

A multinational food technology company with headquarters in Stoney Creek, Ontraio. The company provides AI powered technology for companies across the food and agriculture supply chain.

Describe the project.: 

The project aims to harness a systems engineering perspective to advance the automation of machine learning workflows within the agricultural and food sectors. Specific objectives include:

1. Automated Feature Space Ingestion: Developing methodologies to automatically ingest and process feature spaces from diverse data sources, particularly image data related to food items, both stationary and in motion.
2. Hyperparameter Optimization Automation: Crafting intelligent systems capable of autonomously tuning machine learning models' hyperparameters to optimize performance with limited human intervention.
3. Model Drift Monitoring: Implementing a sophisticated, automated monitoring system to detect model drift in deployed machine learning models. This system will be integral to maintaining the integrity and accuracy of AI applications over time.
4. AI Quality Assurance: Establishing a robust quality assurance process that proactively alerts the engineering team when potential model drifts or performance degradations are detected, ensuring the system's reliability and efficacy in real-time food quality assessment.
5. Algorithmic Enhancement: Continuously refining and tweaking machine learning algorithms to bolster the overall performance of the solutions, ensuring they remain at the cutting edge of technological advancements in the food and agriculture industry.

The role requires a proactive approach to innovation, driving forward the automation and enhancement of machine learning systems to deliver precise and reliable food quality analysis.

Required expertise/skills: 

Machine Learning, Computer Vision, Systems Engineering, Computer Science