classification-modeling
Build binary and multiclass classification models using logistic regression, decision trees, and ensemble methods for categorical prediction and classification
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Build binary and multiclass classification models using logistic regression, decision trees, and ensemble methods for categorical prediction and classification
Launch and configure EC2 instances with security groups, IAM roles, key pairs, AMIs, and auto-scaling. Use for virtual servers and managed infrastructure.
Implement comprehensive health check endpoints for liveness, readiness, and dependency monitoring. Use when deploying to Kubernetes, implementing load balancer health checks, or monitoring service availability.
Implement comprehensive API error handling with standardized error responses, logging, monitoring, and user-friendly messages. Use when building resilient APIs, debugging issues, or improving error reporting.
Create and transform features using encoding, scaling, polynomial features, and domain-specific transformations for improved model performance and interpretability
Maintain comprehensive changelogs and release notes following Keep a Changelog format. Use when documenting version history, release notes, or tracking changes across versions.
Implement service mesh (Istio, Linkerd) for service-to-service communication, traffic management, security, and observability.
Configure HTTP security headers including CSP, HSTS, X-Frame-Options, and XSS protection. Use when hardening web applications against common attacks.
Configure autoscaling for Kubernetes, VMs, and serverless workloads based on metrics, schedules, and custom indicators.
Develop lightweight Flask APIs with routing, blueprints, database integration, authentication, and request/response handling. Use when building RESTful APIs, microservices, or lightweight web services with Flas...
Conduct comprehensive security compliance audits for SOC 2, GDPR, HIPAA, PCI-DSS, and ISO 27001. Use when preparing for certification, annual audits, or compliance validation.
Analyze team capacity, plan resource allocation, and balance workload across projects. Forecast staffing needs and optimize team utilization while maintaining sustainable pace.
Deploy containerized applications on Google Cloud Run with automatic scaling, traffic management, and service mesh integration. Use for container-based serverless computing.
Create and deploy serverless functions using AWS Lambda with event sources, permissions, layers, and environment configuration. Use for event-driven computing without managing servers.
Automate pull request workflows with templates, checklists, auto-merge rules, and review assignments. Reduce manual overhead and improve consistency.
Automated vulnerability detection using OWASP tools, CVE databases, and security scanners. Use when performing security audits, compliance checks, or continuous security monitoring.
Interpret machine learning models using SHAP, LIME, feature importance, partial dependence, and attention visualization for explainability
Analyze temporal data patterns including trends, seasonality, autocorrelation, and forecasting for time series decomposition, trend analysis, and forecasting models
Automate deployments across environments using Helm, Terraform, and ArgoCD. Implement blue-green deployments, canary releases, and rollback strategies.
Optimize cloud storage across AWS S3, Azure Blob, and GCP Cloud Storage with compression, partitioning, lifecycle policies, and cost management.
Implement database sharding for horizontal scalability. Use when scaling large databases, distributing data across multiple servers, or designing sharded architectures.
Debug Docker containers and containerized applications. Diagnose deployment issues, container lifecycle problems, and resource constraints.
Deploy iOS and Android apps to App Store and Google Play. Covers signing, versioning, build configuration, submission process, and release management.
Set up comprehensive infrastructure monitoring with Prometheus, Grafana, and alerting systems for metrics, health checks, and performance tracking.
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。