CoActive AI addresses the challenge of processing unstructured data at scale through AI systems. They identified two key lessons: the importance of logical data models in bridging the gap between data storage and AI processing, and the strategic use of embeddings for cost-effective AI operations. Their solution involves creating data+AI hybrid teams to resolve impedance mismatches and optimizing embedding computations to reduce redundant processing, ultimately enabling more efficient and scalable AI operations.
# Scaling AI Systems for Unstructured Data Processing at CoActive AI
## Company Overview
CoActive AI is focused on building reliable, scalable, and adaptable systems for processing unstructured content, particularly in the visual domain. Their experience comes from three-plus years of user research and two years of system building, leading to valuable insights about implementing AI systems at scale.
## The Evolution of Data Processing
- Shift from structured to unstructured data
## Key Challenges in AI System Design
### Challenge 1: The Data Model Impedance Mismatch
- Traditional Storage vs. AI Requirements
- Organizational Impact
### Challenge 2: Foundation Model Computing Costs
- Scaling Issues
## Solutions and Best Practices
### Logical Data Models Implementation
- Create Data+AI Hybrid Teams
- Multimodal Considerations
### Embedding Optimization Strategy
- Breaking Down the Monolith
- Cost and Performance Benefits
## Technical Implementation Details
### Data Processing Pipeline
- Initial data storage in blob storage
- Transformation layer considering both physical and logical models
- Cached embeddings for repeated computations
- Task-specific output layers
### Scale Considerations
- Text processing: ~40GB for 10M documents
- Video processing: Terabyte-scale data
- Need for specialized tools and approaches for different data modalities
## Best Practices and Recommendations
### Data Engineering
- Design storage systems with AI consumption in mind
- Create clear handoff protocols between data and AI teams
- Implement caching strategies for intermediate computations
### Team Structure
- Form hybrid teams with both data and AI expertise
- Ensure clear ownership of the transformation layer
- Regular collaboration between storage and consumption teams
### Cost Management
- Monitor foundation model usage and costs
- Implement embedding caching strategies
- Regular optimization of computation paths
## Future Directions
- Moving from "data lakes" to "data oceans" due to scale
- Need for specialized tools for visual data processing
- Focus on data-centric approaches to unstructured data
- Emphasis on bridging unstructured to structured data conversion
## Lessons Learned
- Logical data models are crucial for efficient AI operations
- Embeddings can be leveraged for cost optimization
- Hybrid team structures lead to better system design
- Scale considerations must be built into initial system design
- Cost management requires strategic approach to computation
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