Technical Excellence

Real-world DevOps and cloud expertise with production-level workflows. Each skill includes detailed documentation, project examples, and implementation guides.


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EXPERT

GitHub

Version Control & Collaboration Platform

95%

Industry-standard platform for version control and collaborative software development. Essential for modern DevOps practices, enabling teams to collaborate efficiently, maintain code quality, and implement CI/CD pipelines.

Version Control CI/CD Collaboration
PROFICIENT

Azure DevOps & Cloud

Microsoft Cloud Ecosystem

82%

Comprehensive suite for end-to-end software delivery with Azure Boards, Repos, and Pipelines optimized for Microsoft ecosystems. YAML-based pipelines for enterprise applications and ML workflows.

Azure Cloud YAML Pipelines ML Workflows
ADVANCED

GitHub Actions & Pages

Automation & Deployment

85%

YAML-based workflow automation with GitHub Pages for complete development-to-deployment ecosystem. Automated testing, building, and deployment pipelines directly within GitHub repositories.

Workflow Automation YAML Deployment
PROFICIENT

Google Cloud DevOps

Containerized Solutions

80%

Containerized applications and automated builds with Cloud Build, Cloud Run, and IAM for scalable cloud solutions. Serverless container deployments with automated scaling and secure CI/CD pipelines.

GCP Containers Serverless
EXPERT

Docker

Containerization Technology

92%

Containerization technology for consistent application environments from development to production. Dockerfiles, Docker Compose for multi-service architectures, and Docker Swarm for container orchestration.

Containers Orchestration DevOps
PROFICIENT

Kubernetes

Container Orchestration

78%

Container orchestration for automating deployment, scaling, and management of containerized applications. Production Kubernetes clusters with auto-scaling, rolling updates for zero-downtime deployments.

Orchestration Scaling Production
PROFICIENT

MLFlow

ML Lifecycle Management

75%

Open-source platform for managing the complete machine learning lifecycle from experimentation to deployment. MLFlow tracking servers, experiment tracking across cloud platforms, and reproducible ML workflows.

MLOps Tracking Experiments