AI Engineer Portal
Your personal operating system for career transition.
AI Deployment and MLOps
Make AI systems operable beyond the first successful demo.
Containerizing AI applications
Docker for LLM apps, managing large dependencies, multi-stage builds, GPU containers, and model weight management.
Containerizing AI applications
Docker for LLM apps, managing large dependencies, multi-stage builds, GPU containers, and model weight management.
Scaling LLM inference
Load balancing, request queuing, batching for throughput, GPU sharing, and model serving frameworks including vLLM, TGI, and Ollama.
CI/CD for AI features
Testing LLM features in CI, eval gates, golden tests, prompt version control, canary deployments, and feature flags for AI.
Cost management at scale
Token budgets, caching layers including semantic and exact-match cache, model routing for cost optimization, and monitoring spend.
Production reliability
Circuit breakers, fallback chains, graceful degradation, multi-provider failover, and SLAs for AI features.
CI/CD pipelines with eval gates
Add eval gates to your GitHub Actions pipeline so that prompt or model changes that degrade quality are caught before deployment.
Model gateway and routing
Build a model gateway that provides a single interface to multiple providers, with cost-based, latency-based, and capability-based routing plus automatic fallback chains.