Accelerating Object Detection with AWS: Meso AI’s Proof of Concept Success
Accelerating Object Detection with AWS: Meso AI’s Proof of Concept Success
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Meso AI, a marketing acceleration platform, sought to analyze social media images and videos to identify branding and common objects, helping businesses understand consumer preferences. To achieve this, Meso AI engaged SnapSoft to develop a Proof of Concept (PoC) using AWS SageMaker and Serverless YOLO Inference. The PoC successfully demonstrated object detection capabilities, setting the foundation for a more advanced solution in future iterations.
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About the Customer
Meso AI is a marketing acceleration platform designed to help small businesses navigate branding challenges. By leveraging AI-driven insights, Meso AI enhances its clients’ ability to understand market trends, consumer preferences, and branding effectiveness.
Customer Challenges
Meso AI sought to enhance its analytics capabilities by identifying brands and common objects within social media images and videos. However, the company faced several challenges, including the absence of an existing AI infrastructure for object detection, requiring the development of a new model. Additionally, the complexity of analyzing diverse, user-generated content at scale introduced significant data processing challenges. Uncertainty around model performance further necessitated a thorough evaluation of different AI approaches to determine the most effective and cost-efficient solution before committing to full-scale deployment.
Why AWS?
Meso AI chose AWS for its scalability, advanced AI/ML capabilities, and cost-effective solutions. AWS SageMaker offered a managed environment for model training, while Serverless YOLO Inference provided a lightweight and efficient approach for object detection without requiring complex infrastructure.
SnapSoft’s Contribution to the Solution
SnapSoft, an AWS Advanced Tier Services Partner, designed and implemented a Proof of Concept (PoC) to establish an AI-driven object detection solution for Meso AI. The project involved deploying a pre-trained YOLO object detection model using AWS SageMaker and utilizing the Common Objects in Context (COCO) dataset for training and fine-tuning. The model was then deployed in a controlled testing environment to validate its results and assess its potential for future scalability.
AWS Services and Tools Used
- Amazon SageMaker: AI model training and deployment
- Serverless YOLO Inference: Real-time object detection
- Amazon S3: Storage for training datasets
- AWS IAM: Secure access management for project execution
Results and Benefits
The PoC successfully demonstrated AI-powered object detection, providing Meso AI with a strong foundation for future AI-driven branding insights:
- Enhanced Object Recognition: Provided accurate detection of brands and objects in social media content.
- Data-Driven Decision Making: Enabled Meso AI to assess AI’s effectiveness before investing in full-scale deployment.
- Scalability for Future Growth: Established an AI pipeline that can be expanded to more advanced branding and logo detection models.
- Cost-Effective AI Evaluation: Optimized the approach using SageMaker and Serverless YOLO Inference to determine the best path for future AI investments.