Visual Quality Control: Starter Kit for AWS (SageMaker-based)

Today's Challenges Many manufacturers still rely on manual or semi-automated visual inspection processes, which are often inconsistent, labor-intensive, and prone to human error. As product complexity increases and quality standards tighten, especially in industries such as automotive, electronics, and medical devices, companies need more effective ways to detect surface defects, mislabeling, packaging issues, or component irregularities.

Computer vision offers a strong path forward, but implementing these systems can require significant machine learning expertise, custom pipeline development, and infrastructure investment. These challenges can delay time-to-value and create long-term maintenance overhead. Legacy inspection systems may also lack the flexibility to adapt as products evolve or defect patterns change.

Grid Dynamics Solution Grid Dynamics' Visual Quality Control Starter Kit for AWS helps companies quickly implement custom computer vision solutions using Amazon SageMaker and SageMaker JumpStart. This offering provides a fast, sustainable approach to detecting visual defects without needing to build a full machine learning stack from scratch.

The solution includes:

• A preprocessing pipeline for image normalization, augmentation, and labeling

• A model training and deployment workflow using either SageMaker JumpStart templates or custom-built foundation models (Bedrock)

• Real-time or batch inference served through SageMaker Endpoints and orchestrated using AWS Lambda

• Optional dashboards for monitoring defect trends and confidence levels

• Human-in-the-loop feedback loops to support ongoing model refinement

With this solution, customers can improve inspection speed and accuracy, reduce manual workload, and scale to support multiple product lines. It provides a cloud-native foundation that integrates with existing quality control processes and aligns with AWS best practices.

Getting Started Workshop and Customization: Grid Dynamics will host a working session to review your current inspection processes, identify use cases for computer vision, and define data and success requirements.

Platform Deployment: A tailored visual inspection pipeline will be developed in your AWS environment. Grid Dynamics will train and deploy a computer vision model using your image dataset, with optional alerting and dashboarding.

Ongoing Support: Support for model retraining, dashboard updates, and process optimization will continue as your inspection workflows evolve.