AI Stack Evolution:

1.  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

2.  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:

1.  Data Preparation:
    -   Versioned datasets
    -   Curated training data
    -   Smaller but high-quality datasets

2.  Model Training:
    -   Configurable parameters (learning rate, target modules)
    -   Runs on commodity hardware
    -   Hours/days reduced to minutes
    -   ~$20 per training cycle

3.  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.