observability
Distributed tracing with Jaeger, OpenTelemetry, and observability platforms for microservices insights
发现 编程开发 相关 Skills,比较来源仓库和用途,查看适合你工作流的技能说明。
Distributed tracing with Jaeger, OpenTelemetry, and observability platforms for microservices insights
Centralized logging with ELK Stack, Loki, Fluentd, and log analysis for distributed systems
Docker and Kubernetes - containerization, orchestration, and production deployment.
GitOps practices with ArgoCD, FluxCD, and declarative infrastructure management
Service mesh technologies including Istio, Linkerd, Consul, and Envoy for microservices communication
Implement comprehensive AWS security controls and compliance
Deploy and configure RDS/Aurora databases with HA and security
Design and implement production-grade VPC architectures
Launch, configure, and manage EC2 instances with best practices
Configure AWS IAM users, roles, policies, and identity federation
Optimize AWS costs through resource rightsizing and savings strategies
Configure S3 buckets with security, lifecycle, and replication policies
Infrastructure as Code with CloudFormation templates and stacks
Implement monitoring, alerting, and observability with CloudWatch
Deploy and manage containerized applications on ECS/Fargate
Build optimized serverless functions with Lambda
Build automated CI/CD pipelines with CodePipeline and CodeBuild
Retrieval Augmented Generation systems with vector search, document processing, and hybrid retrieval.
LLM evaluation frameworks, benchmarks, and quality metrics for production systems.
AI agent development with LangChain, CrewAI, AutoGen, and tool integration patterns.
LLM architecture, tokenization, transformers, and inference optimization. Use for understanding and working with language models.
Vector database selection, indexing strategies, and semantic search optimization.
LLM deployment strategies including vLLM, TGI, and cloud inference endpoints.
LLM fine-tuning with LoRA, QLoRA, and instruction tuning for domain adaptation.
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。