Build and deploy production-grade AI/ML systems with MLOps pipelines.
npx skills add https://github.com/Exia-thd/Digital-Nervous --skill ai-engineer-exia-thd
Please help me install this Agent Skill. Skill: ai-engineer Source: https://github.com/Exia-thd/Digital-Nervous/tree/main/skills/ai-engineer Command: npx skills add https://github.com/Exia-thd/Digital-Nervous --skill ai-engineer-exia-thd
Enterprises need reliable, scalable AI/ML pipelines that move from model selection and fine‑tuning to serving, monitoring, and cost management, while ensuring quality and robustness.
Ask the AI Engineer to design and launch a production‑grade MLOps pipeline for your new language model.
High-intent search queries and answers about installing and using this skill.
To build a production-grade MLOps pipeline, you need end-to-end automation covering data preprocessing, training, versioned registries, A/B testing, and continuous monitoring. This ensures reliable, scalable AI/ML pipelines from model selection to serving.
Optimizing RAG pipelines for enterprise environments involves implementing hybrid search with reranking, TTL-based embedding refresh, and evaluation metrics such as RAGAS to ensure retrieval quality and robustness.
Model benchmarking and routing compares multiple models for cost, latency, and quality, automatically routing requests to the optimal provider to ensure scalable AI/ML systems with built-in fallback mechanisms.
You can automate evaluation and monitoring using automated test suites, LLM-as-judge assessments, cost tracking, latency alerts, and quality drift detection to maintain robust production-grade AI/ML systems.
Automated evaluation suites are needed for ML pipelines when moving models to production, requiring continuous monitoring, cost tracking, and quality drift detection to ensure reliable, scalable enterprise AI services.
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tranhieuttBuild production ML systems with PyTorch and TensorFlow for serving and monitoring.
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ouakarDesign and deploy production AI/ML systems with MLOps pipelines and monitoring.
