Digital publishers use machine learning for faster content creation, ensuring relevant images match articles. Amazon’s Titan Multimodal Embeddings model generates image and text embeddings for semantic search. This streamlines finding appropriate images, without keywords, by comparing metadata similarity—enhancing media workflows while maintaining quality. Amazon Bedrock simplifies AI application development for various modalities.
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Enhance Your Digital Publishing with AI
Streamline Content Creation: Automate your media workflows to produce content quickly without compromising quality.
Improve Reader Engagement: Use machine learning to find images that complement your text, enhancing the reading experience.
Amazon’s AI Services for Image Discovery
Previously, we showed how to use Amazon Rekognition for image metadata extraction. Now, discover how Amazon Titan foundation models can quickly match articles with the perfect images.
Understanding Embeddings
Embeddings are numerical vectors representing images or text. By comparing these vectors, we find semantically similar content.
Amazon Bedrock and Titan Multimodal Embeddings
Amazon Bedrock provides a range of AI models for generative applications. The new Titan Multimodal Embeddings model understands both text and images, perfect for searches and recommendations.
Overview of the Solution
Upload an article and find related images without manual keyword entry. Amazon services automate metadata extraction and image search, delivering relevant visuals for your content.
Walkthrough
Follow these steps to set up and use the AI-powered image search:
- Upload images to Amazon S3.
- Extract metadata using Amazon Rekognition.
- Generate image embeddings with Titan Multimodal Embeddings.
- Store metadata in OpenSearch Service.
- Submit an article to find matching images.
- Use Amazon Comprehend and Titan Text G1 – Express to process the article.
- Search for images using the article’s embedding and metadata.
Prerequisites
Before starting, ensure you have an AWS account, AWS SAM CLI, Docker, Node, and npm installed.
Build and Deploy
Clone the repository, install dependencies, and deploy your application with provided scripts. Access the application through the Amazon CloudFront URL.
Cleaning Up
To avoid charges, delete the AWS resources used for this solution.
Conclusion
Amazon’s AI services offer a powerful way to enhance content with relevant images, crucial for publishers needing speed and quality. Deploy this solution and see how semantic search can benefit your business.
About the Authors
Mark Watkins and Dan Johns are Solutions Architects at AWS, helping customers with data and ML challenges.
Transform Your Business with AI
Stay competitive and harness AI for your advantage. Build semantic image search capabilities with Amazon Titan and redefine your workflows.
- Identify automation opportunities in customer interactions.
- Define clear KPIs to measure AI’s impact on your business.
- Select AI solutions that meet your specific needs.
- Implement AI gradually, starting with a pilot program.
For AI KPI management advice, email us at hello@itinai.com. Follow us on Telegram t.me/itinainews or Twitter @itinaicom for continuous AI insights.
Spotlight on a Practical AI Solution:
Explore the AI Sales Bot at itinai.com/aisalesbot, designed to enhance customer engagement and manage interactions throughout their journey.
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