AI Infrastructure
LLM Evals & Synthetic Testing: Ragas, DeepEval, and CI/CD Quality Gates
Prevent regressions in production AI applications with automated evaluation frameworks, synthetic test datasets, and LLM-as-a-Judge pipelines.
Continuous AI Quality Gates in CI/CD
Automated evals score faithfulness, answer relevance, and toxicity across every prompt tweak or model upgrade before deploying to production.
Related Technical Guides
Deepen your understanding with these closely related production architectures and tutorials:
Llama 4 MoE & DeepSeek-R1: 24GB GPU Hardware Limits, vLLM PagedAttention & think Filtering
Deploy DeepSeek-R1 distilled weights and evaluate Meta Llama 4 Scout/Maverick MoE hardware realities on single 24GB GPUs with vLLM and FastAPI.
DeepSeek-R1 & Open-Source Reasoning: Self-Hosting with Ollama, vLLM, and Enterprise GPU Deployment
Deploy DeepSeek-R1 and distilled open-weight reasoning models locally with vLLM or Ollama for zero-API-cost private reasoning engines and air-gapped data privacy.
Multi-Model Management with LiteLLM: Unified APIs, Automatic Fallback & Load Balancing
Combine OpenAI, Anthropic, Gemini, Bedrock, and local models under a single standardized interface with automatic retry and rate-limit routing.