Tasks
- esign and optimize ML pipelines for manufacturing.
- Develop scalable cloud infrastructure for ML.
- Prepare, clean, and annotate data for quality training.
- Implement CI/CD pipelines for ML models.
Leading AI-powered co-pilot solutions for manufacturing and assembly
Challenges
The main challenge was to create a robust, scalable ML pipeline to process large volumes of manufacturing data in real-time, ensuring reliability and efficiency to support AI-driven co-pilot solutions for operator assistance.
Solutions
- Built a complete ML pipeline for data processing, training, and deployment.
- Enhanced scalability and reduced costs with cloud resources.
- Developed data cleaning tools for high-quality datasets.
- Integrated CI/CD for seamless model updates.
Outcomes
- Implementing the ML pipeline boosted manufacturing efficiency and accuracy, while the nFlux Guide™ enhanced operators’ achievement, safety, and reduced stress, leading to greater productivity.
Technologies Used
Python
Tensorflow
PyTorch
AWS
Azure
Docker
