Searching images through patterns: An AI-powered serverless solution (DEV204)

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Current State of Generative Models

• Tremendous advancements in the last two years (2023-2024) with new generation of models from companies like Meta, OpenAI, Anthropic, etc.
• Newer models have better capabilities, higher quality, and are more cost-effective.
• Key advancements:
  • Improved GPU technology enabling scaling of models to billions of parameters.
  • Advancements in data sets and training techniques, including use of synthetic data and multilingual models.

Key Features of Newer Models

• Larger context windows (up to 300,000 tokens)
• Multimodality - ability to process and generate content in text, image, audio, and video formats
• Improved reasoning and inference capabilities, going beyond simple question answering
• Agentic workflow - models becoming intelligent agents capable of interacting with external systems and performing autonomous actions

Text Models vs. Multimodal Models

• Text models are designed to ingest and generate text based on patterns in textual data.
• Multimodal models can process information from multiple modalities (text, image, audio, video) and integrate visual and auditory context.

Zero-Shot Prompting

• Zero-shot prompting allows models to perform tasks immediately without requiring prior examples or task-specific training.
• Benefits for business use cases:
  • Extracts information without needing previous examples
  • Allows faster implementation of new applications and features
  • Saves time and cost on data preparation and model training

Visual Examples

• Visual question answering
• Diagram interpretation
• Image captioning
• Grounding (identifying object locations in an image)

Customer Use Case

• Digital printing company that creates and prints designs on fabrics for garments.
• Key challenges:
  • Large, unstructured design file repository (few terabytes)
  • Manual process for creating mood boards and finding inspiration images
  • Reliance on external resources for images, incurring additional costs
• Requirements:
  • Create a searchable attribute database of design files
  • Maintain privacy and security of the design files
  • Implement a low-cost, low-maintenance solution

Solution Approach

1. Pre-processing on-premises: Segmenting and sampling images on local infrastructure to reduce processing costs.
2. Generative AI model: Using Anthropic's Clover 3 (Haiku) model on AWS Bedrock for attribute extraction, instead of a machine learning model.
3. Serverless Architecture:
   • Uploading image segments to S3
   • Processing images and storing attributes in Aurora Serverless database
   • Implementing a searchable interface using API Gateway, Lambda, and DynamoDB

Key Takeaways

• Rapid development and implementation of the solution using serverless and managed services.
• Generative AI models proved effective in extracting accurate attributes from a large, unstructured design file repository.
• Pay-as-you-go pricing model and leveraging free tiers helped keep the solution cost-effective.
• Constant feedback and a simplified, low-maintenance approach were crucial for the successful implementation.
• Newer capabilities like prompt routing in services like Bedrock open up more possibilities for future use cases.
• Cost is a significant factor, and intelligent use of cloud infrastructure can help reduce costs drastically.