From a single endpoint to a battle-tested AI platform — the skills that get you promoted.
3 phases. One platform. Deploy Mistral-7B → optimize cost & latency → scale to 100 users → harden for production with SRE-grade observability.
Start with Part 1 (~2 hrs)
fig 1 — finsight ai serving platform
LATENCY
<200ms
P99 Target
THROUGHPUT
100+
Concurrent Users
COST CUT
40%
Via Caching + Batching
RELIABILITY
99.9%
With Circuit Breakers
A production AI serving platform that handles real traffic, real costs, and real failures — the kind that gets into staff-level portfolios.
Serve Mistral-7B with vLLM, wrapped in FastAPI, containerized with Docker, and load-tested with Locust to validate P99 latency targets
Integrate pgvector retrieval with semantic caching in Redis, calibrate the cost vs accuracy tradeoff, and log traces for every request
Autoscale workers with Ray Serve, load-balance across replicas with Nginx, and stream token-by-token responses over SSE
Full Prometheus/Grafana dashboard with per-request cost modeling, circuit breakers, distributed tracing, and a "Break the System" lab
3 phases. Each phase builds on what you shipped in the previous one.
You can serve a model. Can you run it in production?
Most AI systems fail not because of the model:
Most engineers stop at "I deployed a model." The engineers who go further own the platform.
Career Signal
AI Platform Engineers with production serving experience command $40–80K more than ML engineers who only fine-tune models.
Inevitability
If your company ships AI products, someone needs to own serving, cost, and reliability. That person gets promoted.
→ This is the difference between "I deployed a model" and "I own the AI platform."
Expert tier unlocks
Production patterns you'll implement across the platform.
Dynamic batching, KV cache tuning, and warm-pool strategies to hit sub-200ms P99 under real production load
Semantic caching, model routing, and batching optimizations to cut per-request cost without compromising answer quality
Circuit breakers, autoscaling, health probes, and a formal failure runbook to maintain SLA under GPU contention and traffic spikes
Spin up the full serving stack and run your first benchmark in under 5 minutes.
# Clone the project & launch the serving stack$ git clone https://github.com/aide-hub/finsight-serving.git$ cd finsight-serving# Start vLLM + Redis + Prometheus + Grafana$ docker-compose -f docker-compose.serving.yml up -d# Deploy Mistral-7B and run first benchmark$ python -m serving deploy \$ --model mistralai/Mistral-7B-Instruct-v0.2 \$ --backend vllm --port 8000 \$ --benchmark locust --users 20 --duration 60s
Pair this project with the AI Inference & Serving Systems skill toolkit for a complete understanding of both theory and production implementation.
AI Inference & Serving SystemsPhase 3 Staff Capstone produces a 5-deliverable portfolio piece: architecture diagram, failure runbook, circuit breaker implementation, cost model, and a live demo under Locust load. This is the kind of work interviewers at Anthropic, OpenAI, and Mistral look for when hiring AI Platform Engineers.
An AI serving platform is the production infrastructure that deploys, scales, and monitors machine learning models behind low-latency APIs. This project builds a complete serving stack using vLLM for optimized inference, Ray Serve for horizontal scaling, FastAPI for the API layer, and Prometheus/Grafana for observability, culminating in a multi-model platform that routes requests based on complexity, cost, and latency requirements.
Deploy Mistral-7B behind a production FastAPI endpoint and benchmark under real load
Implement batching, quantization, and KV-cache optimization for 3x throughput improvement
Build a multi-model serving platform with intelligent routing and A/B testing
Add production observability with Prometheus metrics, Grafana dashboards, and alerting
Deploy to Kubernetes with auto-scaling, canary rollouts, and failure recovery
Every company deploying AI needs a serving layer that handles real traffic reliably. Companies like Anthropic use vLLM and Ray Serve in production. The difference between a demo and a production AI system is the serving infrastructure -- batching, caching, routing, and monitoring. This is the system that sits between your model and your users.
| Aspect | Traditional | This Project |
|---|---|---|
| Inference Speed | Single-request processing, high latency | Batched inference with vLLM, 3x throughput |
| Scaling | Single server, manual scaling | Ray Serve auto-scaling on Kubernetes |
| Model Management | One model, no versioning | Multi-model routing with canary rollouts |
| Monitoring | Basic logs only | Prometheus/Grafana with latency, throughput, cost tracking |
Start with Phase 1: deploy your first AI endpoint and hit your first latency target (~2 hrs)