Building ML pipelines requires expertise in model deployment, monitoring, and MLOps practices. This skill provides production-ready tools for deploying ML models, integrating LLMs, and building scalable RAG systems with enterprise-grade reliability.
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正在使用“senior-ml-engineer”。 Help me deploy my trained TensorFlow model for real-time inference with sub-50ms latency
预期结果:
- Containerize the model using Docker with TensorFlow Serving
- Create Kubernetes deployment with HPA for auto-scaling
- Implement batch inference for throughput optimization
- Add Prometheus metrics for latency, throughput, and error rates
- Configure load balancer with health checks and circuit breakers
正在使用“senior-ml-engineer”。 Build a RAG system that queries my internal knowledge base with citation support
预期结果:
- Set up embedding model and vector database (Pinecone or Weaviate)
- Implement document chunking with overlap for context preservation
- Create hybrid search combining semantic and keyword matching
- Add source citation and confidence scoring to responses
- Implement re-ranking for improved result quality
安全审计
安全All 78 static findings evaluated as false positives. The static scanner incorrectly flagged documentation text (headings, bullet points) containing common English words as 'weak cryptographic algorithm' patterns. Python scripts contain only standard library imports and logging. Bash code blocks in SKILL.md are documentation examples, not shell execution. No malicious code exists in this skill.
风险因素
质量评分
你能构建什么
Deploy models to production
Package and deploy trained ML models with Docker, Kubernetes, and REST API endpoints
Build RAG applications
Create retrieval-augmented generation systems using LlamaIndex and LangChain
Implement MLOps
Set up model versioning, monitoring, and automated retraining pipelines with MLflow
试试这些提示
Help me deploy my trained PyTorch classification model to production. I need a REST API endpoint, Docker container, and Kubernetes deployment configuration. Include monitoring for model latency and accuracy drift.
Build a RAG system for my documentation. I have PDF and markdown files. Use a vector database, implement chunking strategies, and create a query interface that works with my internal LLM API.
Set up MLOps for my machine learning team. We need MLflow for model versioning, automated training pipelines triggered by data changes, and alerts for model performance degradation.
Design an enterprise LLM integration architecture. Include prompt caching, rate limiting, cost tracking by team, and integration with our existing authentication system. Support both OpenAI and self-hosted models.
最佳实践
- Design for 10x scale from day one with horizontal scaling architecture
- Implement comprehensive monitoring including latency, drift detection, and data quality checks
- Use feature stores to ensure consistency between training and inference
避免
- Deploying models without version control or rollback capabilities
- Skipping model drift detection until production issues arise
- Hardcoding API keys or credentials instead of using secrets management
常见问题
What ML frameworks does this skill support?
Can I use this for LLM fine-tuning?
Does this include cloud credentials?
What monitoring tools are integrated?
Can I deploy on-premises?
How do I handle model updates?
开发者详情
许可证
MIT
引用
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