持續整合/部署
13574 skills in DevOps > 持續整合/部署
ML Model Training
Build and train machine learning models using scikit-learn, PyTorch, and TensorFlow for classification, regression, and clustering tasks
user-persona-creation
Create detailed user personas based on research and data. Develop realistic representations of target users to guide product decisions and ensure user-centered design.
load-balancer-setup
Configure and deploy load balancers (HAProxy, AWS ELB/ALB/NLB) for distributing traffic, session management, and high availability.
agile-sprint-planning
Plan and execute effective sprints using Agile methodologies. Define sprint goals, estimate user stories, manage sprint backlog, and facilitate daily standups to maximize team productivity and deliver value incrementally.
design-handoff
Prepare designs for development handoff. Document specifications, interactions, and assets to enable efficient development and maintain design quality.
api-pagination
Implement efficient pagination strategies for large datasets using offset/limit, cursor-based, and keyset pagination. Use when returning collections, managing large result sets, or optimizing query performance.
app-store-deployment
Deploy iOS and Android apps to App Store and Google Play. Covers signing, versioning, build configuration, submission process, and release management.
static-code-analysis
Implement static code analysis with linters, formatters, and security scanners to catch bugs early. Use when enforcing code standards, detecting security vulnerabilities, or automating code review.
mobile-app-debugging
Debug issues specific to mobile applications including platform-specific problems, device constraints, and connectivity issues.
release-guide-info
Generate Ops Update Guide from Git Diff. Produces internal Operations-facing update/migration guides based on git diff analysis. Supports STRICT_NO_TOUCH (default) and TEMP_CLONE_FOR_FRESH_REFS modes. Includes tag auto-detection and commit log analysis.
root-cause-tracing
Backward call-chain tracing - systematically trace bugs from error location back through call stack to original trigger. Adds instrumentation when needed.
Classification Modeling
Build binary and multiclass classification models using logistic regression, decision trees, and ensemble methods for categorical prediction and classification
Dimensionality Reduction
Reduce feature dimensionality using PCA, t-SNE, and feature selection for feature reduction, visualization, and computational efficiency
disaster-recovery-testing
Execute comprehensive disaster recovery tests, validate recovery procedures, and document lessons learned from DR exercises.
technical-debt-assessment
Assess, quantify, and prioritize technical debt using code analysis, metrics, and impact analysis. Use when planning refactoring, evaluating codebases, or making architectural decisions.
using-tw-team
Technical writing specialists for functional and API documentation. Dispatch when you need to create guides, conceptual docs, or API references following established documentation standards.
azure-app-service
Deploy and manage web apps using Azure App Service with auto-scaling, deployment slots, SSL/TLS, and monitoring. Use for hosting web applications on Azure.
Feature Engineering
Create and transform features using encoding, scaling, polynomial features, and domain-specific transformations for improved model performance and interpretability
ML Pipeline Automation
Build end-to-end ML pipelines with automated data processing, training, validation, and deployment using Airflow, Kubeflow, and Jenkins
user-research-analysis
Analyze user research data to uncover insights, identify patterns, and inform design decisions. Synthesize qualitative and quantitative research into actionable recommendations.