Welcome to the documentation for a production-ready AI infrastructure project. This series documents the journey from a raw Whisper model to a fully containerized, agent-orchestrated transcription ecosystem.
Tip
Source Code: You can find the implementation of our high-performance Twi ASR inference server in its dedicated repository: 🔗 custom-twi-asr-inferencer
- AI Engine: OpenAI Whisper (Small) fine-tuned for Twi (Serlabs).
- Inference Orchestration: BentoML, PyTorch, Librosa.
- Agent Architecture: ADK (Agent Development Kit), Docker Sandboxes.
- Infrastructure: Docker Compose, GitHub-ready CI/CD patterns.
Learn how to transform heavy ML models into lightweight, high-performance microservices.
- The Strategy: Why models belong in containers.
- Implementation: Building the Twi ASR server with BentoML.
- Deep Dive: Whisper Twi: Model architecture and FP16 optimizations.
- Scaling & Ops: Health checks, resource limits, and RTF tracking.
Moving from "Chat" to "Action" by providing agents with safe workbenches. 5. Why Sandboxing?: Solving the security risks of LLM code execution. 6. Building the Workbench: Ephemeral Docker environments for agents. 7. Advanced Isolation: Managed state, network egress, and human-in-the-loop.
The "Holy Grail"—connecting the brain to the workbench. 8. The Agentic Bridge: Orchestrating inference models via sandboxed agents. 9. One-Click Deployment: Launching the full stack with Docker Compose.
- Security-First: Implemented strict Docker isolation for AI-generated code.
- Optimized Performance: Reduced inference latency using FP16 and optimized base images.
- Production Ready: Full support for health monitoring, RTF metrics, and horizontal scaling.
- Low-Resource Focus: Specialized support for Twi ASR, bridging the gap for under-represented languages.
Created with ❤️ by teckedd