Meharaz Hossain
meharaz733
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AI & ML interests
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Recent Activity
reacted
to
TravisMuhlestein's
post with 👍 9 days ago
Moving AI from experiments to production systems (GoDaddy + AWS case study)
A recurring pattern across many organizations right now is that AI experimentation is easy — operationalizing it is much harder.
This case study from AWS describes how GoDaddy has been deploying AI systems in production environments using AWS infrastructure.
One example is Lighthouse, a generative AI system built using Amazon Bedrock that analyzes large volumes of customer support interactions to identify patterns, insights, and opportunities for improvement.
The interesting part isn’t just the model usage — it’s the system design around it:
- large-scale interaction data ingestion
- LLM-driven analysis pipelines
- recursive learning platforms where real-world signals improve systems over time
- infrastructure designed for continuous iteration
We’re starting to see a shift where organizations move from AI prototypes toward AI platforms and production systems.
Would be interested to hear how others in the community are thinking about:
- production AI architectures
- LLM evaluation pipelines
- Feedback loops in real-world systems
- infrastructure for scaling AI workloads
Case study:
https://aws.amazon.com/partners/success/godaddy-agenticai/
reacted
to
perfecXion's
post with 👍 9 days ago
# IntentGuard: Open-Source Vertical Intent Classifiers for LLM Guardrails
Three models published to the Hub:
- [perfecXion/intentguard-finance](https://huggingface.co/perfecXion/intentguard-finance)
- [perfecXion/intentguard-healthcare](https://huggingface.co/perfecXion/intentguard-healthcare)
- [perfecXion/intentguard-legal](https://huggingface.co/perfecXion/intentguard-legal)
DeBERTa-v3-xsmall fine-tuned for three-way classification: **allow**, **deny**, or **abstain**. ONNX + INT8 quantized, under 80MB, p99 <30ms on CPU. Margin-based thresholds (not argmax) — uncertain queries route to clarification instead of forcing a guess.
**Eval results (adversarial test sets, ~470-480 examples per vertical):**
| Vertical | Accuracy | Legit-Block Rate | Off-Topic-Pass Rate |
|----------|----------|------------------|---------------------|
| Finance | 99.6% | 0.00% | 0.00% |
| Healthcare | 98.9% | 0.00% | 0.98% |
| Legal | 97.9% | 0.00% | 0.50% |
```bash
docker run -p 8080:8080 ghcr.io/perfecxion/intentguard:finance-latest
curl -X POST http://localhost:8080/v1/classify \
-H "Content-Type: application/json" \
-d '{"messages": [{"role": "user", "content": "What are current mortgage rates?"}]}'
```
Apache 2.0. Full pipeline + Docker configs on [GitHub](https://github.com/perfecxion-ai/intentguard).
Feedback welcome on domain coverage, adversarial robustness, and multilingual demand.
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meharaz733/ChoritroAI
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#3351 opened 8 months ago
by
meharaz733