## 💡 Key Technical Implementation Details
Table of Contents
AI Stack Evolution:
- Initial Stack (AWS-based):
- SageMaker powering 60+ indicators
- First LLM implementation in 2019
- Transition to Longformer in 2021 for extended context
- Individual auto-scaling infrastructure per model
- Current Architecture (PredaBase + LoRA):
- Base Model: Llama 3.18B
- 60+ LoRA adapters on single GPU
- Hybrid setup with private VPC + managed scaling
- Sub-second inference times (0.1s achieved)
📈 Performance Metrics
Comparative Analysis:
- Cost: 10x reduction vs OpenAI
- Accuracy: 8% higher F1 score
- Throughput: 80% higher than alternatives
- Latency: 0.1 second inference time (vs 2s target)
- Scale: Hundreds of inferences per second
Infrastructure Requirements:
- Rapid scaling (within 1 minute)
- On-demand GPU provisioning
- Support for variable text lengths (2min - 1hr calls)
- Handling unpredictable traffic patterns
🤖 Technical Improvements
Training Pipeline:
- Data Preparation:
- Versioned datasets
- Curated training data
- Smaller but high-quality datasets
- Model Training:
- Configurable parameters (learning rate, target modules)
- Runs on commodity hardware
- Hours/days reduced to minutes
- ~$20 per training cycle
- Deployment:
- Configuration-based deployment
- Simultaneous version running
- Easy A/B testing
- Zero marginal cost per adapter
📋 Monitoring & Operations
System Monitoring:
- Throughput tracking
- Latency measurements
- Model drift detection
- Combined dashboard system (PredaBase + Converza)
Cost Analysis:
- Linear cost scaling with PredaBase
- Exponential cost increase avoided
- Near-zero marginal cost per adapter
- Infrastructure costs primarily tied to throughput/latency requirements
The implementation demonstrates successful migration to small language models while achieving better performance metrics and significant cost savings, particularly in scaling scenarios.