Skill UI

Use Declarative Automation Bundles (DABs) to manage and deploy Databricks assets, including jobs, DLT pipelines, and ML models, using an Infrastructure-as-Code approach. This skill allows defining resources and deployment targets (dev, staging, prod) in a single YAML file, enabling robust CI/CD practices and controlled promotion of changes across environments.

This comprehensive checklist guides the deployment of Databricks data pipelines and jobs to a production environment. It covers critical stages including pre-deployment security hardening (Service Principals, Unity Catalog), validating infrastructure (cluster types, autoscaling), ensuring code quality (unit/integration tests, Delta Lake best practices), executing controlled deployment, and performing post-deployment health checks and monitoring setup. Ideal for CI/CD pipelines and go-live procedures.

Deploying Machine Learning Models automates serving trained models to production by analyzing requirements, generating API and infrastructure code, and orchestrating deployment workflows with validation, error handling, and monitoring baked in.

This skill defines a senior engineer specializing in the entire software development lifecycle (SDLC). It covers building robust CI/CD pipelines (e.g., GitHub Actions), containerizing applications with Docker, and managing infrastructure using Infrastructure as Code (IaC) tools like Terraform or Pulumi. Key areas include Kubernetes deployment (GitOps), cloud platform configuration (AWS/GCP/Azure), release automation, and incident response planning.

Provides practical advice on Dockerfiles, CI/CD pipelines, deployment strategies, infrastructure-as-code, and observability, helping teams containerize apps, configure GitHub Actions, plan production rollouts, and keep deployments secure and observable.

Guides DevOps users through managing environment variables with best practices, code/config generation, and validation while covering foundational version control, containerization, CI/CD, and infrastructure topics.

Scans infrastructure-as-code, application, and system configurations via the security-misconfiguration-finder plugin to expose vulnerabilities and compliance gaps, enabling proactive remediation before deployment.

Generates complete, production-ready Google Cloud Platform (GCP) code examples and infrastructure templates. It spans various modern AI patterns, including ADK agents, Genkit RAG flows, and multimodal analysis using Vertex AI and Gemini. The output includes runnable code, deployment configuration (Terraform/IaC), security best practices (IAM, VPC-SC), and monitoring setup, ensuring enterprise-grade reliability and cost optimization.

Generates production-ready Terraform, CloudFormation, Pulumi, ARM, or CDK templates via the infrastructure-as-code-generator plugin to automate cloud infrastructure deployment across AWS, Azure, and GCP from user specifications.

Automated DevOps helper for Git workflow manager tasks, triggering on mentions to offer best practices, code and configuration generation, and validation across version control, containerization, CI/CD, and foundational infrastructure topics.

A comprehensive guide featuring best practices and patterns for building robust GitLab CI/CD pipelines. Covers multi-stage deployments, containerization (Docker), infrastructure as code (Terraform), security scanning, caching strategies, and advanced GitOps workflows for automated testing and deployment to Kubernetes.

Automates helm chart generation, guidance, and validation for advanced DevOps workflows, covering Kubernetes, Terraform, monitoring, and infrastructure-as-code best practices.