Smart Manufacturing: Seagate's Global Deployment of Defect Detection System with MLOps Automation

• Analytics & Modeling - Machine Learning
• Infrastructure as a Service (IaaS) - Hybrid Cloud

• Cement
• Education

• Facility Management
• Product Research & Development

• Construction Management
• Infrastructure Inspection

• Cloud Planning, Design & Implementation Services
• Training

Seagate Technology, a global leader in data storage and management solutions, faced significant challenges in deploying a defect detection system across their global manufacturing facilities. The system had the potential to improve ROI by 300%, significantly reducing time processing defects and at a much lower cost. However, Seagate's legacy workflows made it difficult to deploy their model at scale. The team experienced low efficiency at many stages of the workflow due to manual tasks that prolonged the workflow, causing bottlenecks within the pipeline. Seagate was also experiencing low server utilization of their hybrid cloud infrastructure, as they had to run each workload separately, and did not have a mechanism in place to run different workloads on optimal machines. The team required an infrastructure to automate the pipeline components, such that the resources will be scheduled automatically, in real-time with maximum efficiency. At the production level, Seagate required advanced deployments that could serve on TensorFlow and Kafka endpoints.

Seagate Technology is a global leader in data storage and management solutions with over 40 years of experience. The company's technology has transformed business results across sectors, powering AI/ML initiatives, modernizing backup infrastructure, and delivering private cloud solutions. Seagate’s team of data science professionals and machine learning engineers build advanced deep learning scripts to solve business problems and drive results. They have built a defect detection system to be deployed globally across their manufacturing facilities, which has the potential to significantly improve ROI, reduce time processing defects, and lower costs.

Seagate's Advanced Analytics Group partnered with cnvrg.io to update their infrastructure with MLOps automation and successfully deliver the defect detection system globally. cnvrg.io offered an optimal AI solution for Seagate’s Advanced Analytics Group, designing an end-to-end flow, which would be automatically executed. This technology unified their workflow and connected their hybrid cloud infrastructure allowing them to run multiple clouds at the same time in one view. Seagate used cnvrg.io to streamline and accelerate ML pipelines and improve utilization of resources for all their AI projects. cnvrg.io enabled Seagate to automate ML pipeline components, such that their hybrid cloud resources were scheduled automatically, in real-time with maximum efficiency. The solution also offered model training and evaluation, model management, model deployment, collaboration at scale, model monitoring, model retraining, and data management.

• Using cnvrg.io, Seagate was able to transform their legacy AI workflow into a scalable modern automated pipeline. This transformation allowed them to address more business use cases and improved the efficiency of their data scientists. The automated pipeline maintains optimized performance and delivers the ability to release and manage models in production using TensorFlow endpoints and Kafka endpoints seamlessly. Seagate can now run and manage hundreds of experiments in parallel on optimized compute, and support model serving with canary rollout to deliver peak performing models. The collaboration with cnvrg.io also enabled Seagate to achieve a successful scalable AI deployment across global facilities, improved collaboration globally across advanced analytics, engineering and IT teams, and decreased IT technical debt with MLOps capabilities.

• Improved ROI by 300%

• Accelerated ML pipeline by 50%

• Increased data scientists efficiency by up to 50%