'pipelines' 검색 결과

Manage Azure Machine Learning resources from Python with SDK v2. The skill provides implementation examples for registering data and models, configuring compute and environments, submitting training jobs, monitoring logs, and connecting steps into pipelines.

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• Submitting training jobs and monitoring logs
• Registering data and model versions
• Connecting preprocessing and training steps

Design connections between APIs and services, including webhooks, which notify other systems when events occur. Define event formats, retries, duplicate prevention, and recovery steps for workflows such as order processing and data pipelines.

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• Designing webhook notifications between services
• Connecting authentication, orders, and payments
• Planning recovery when a multi-service workflow fails

Plan AI application development by selecting relevant phases and companion skills. The workflow covers language model integration, retrieval from documents, agents, machine learning pipelines, monitoring, and security, with prompts and quality checklists.

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• Plan AI features and language model integration
• Organize RAG retrieval design and implementation
• Map agent roles, tools, and memory

Write more reliable Bash scripts for production automation, CI/CD pipelines, and system administration. Covers input validation, error handling, structured logs, safe temporary files, dry runs to preview changes, and scripts designed for safe reruns.

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• Writing production automation scripts
• Improving error handling in CI/CD scripts
• Adding dry runs to deployment scripts

Create automated shell script tests with Bats, a Bash testing tool. Check exit codes, output, and failure cases, organize reusable test helpers and data, and run checks across shells or in CI/CD pipelines.

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• Add automated tests to shell scripts
• Check invalid input and missing file errors
• Test behavior in bash, sh, and dash

Build data quality checks with Great Expectations, dbt tests, and data contracts, which define shared expectations between teams. Check missing values, duplicates, and freshness, then integrate validation into data pipelines and CI/CD.

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• Check data for missing values and duplicates
• Build a dbt test suite
• Define shared data contracts between teams

Manage API keys, passwords, and certificates used in CI/CD, the automated process for testing and deploying software. Connect secret stores, plan credential rotation, restrict access, and prevent sensitive values from appearing in logs.

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• Moving API keys and passwords out of source code
• Connecting CI/CD pipelines to secret stores
• Planning automated or manual credential rotation

Build systems that use Pinecone to find documents for AI answers or retain agent memories across sessions. Guides index setup, data loading, meaning-based and keyword search, memory separation, and end-to-end checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an AI assistant that answers from documents
• Keep agent facts and decisions across sessions
• Combine meaning-based search with exact terminology

Design and review infrastructure and release workflows with checks tailored to the environment. Covers automated build and deployment pipelines, containers, health checks, rollback plans, feature flags, and mobile releases to help assess readiness before deployment.

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• Reviewing deployment and rollback plans
• Designing automated build and deployment pipelines
• Checking Docker and Kubernetes configurations

Review Python and BigQuery pipelines for excessive costs, duplicate writes on reruns, and hard-to-diagnose failures. Receive a structured assessment with specific function and line references, prioritized fixes, and rough estimates of the largest cost risks.

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• Check BigQuery pipeline code before production use
• Estimate job counts and cost risks in loops
• Review duplicate writes when rerunning failed jobs

Audit GitHub Actions workflows and CI/CD configurations for supply chain risks. Use Python and YAML parsing to identify unpinned actions, unsafe event expressions, excessive permissions, dependency confusion, and potential exposure of secrets in build pipelines.

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• Reviewing GitHub Actions workflow security
• Checking action version pinning
• Finding unsafe event expressions in scripts

Build automated pipelines that collect threat indicators, such as malicious IP addresses and file hashes, from multiple feeds. Standardize formats, remove duplicates, assess confidence, and distribute indicators to security monitoring systems for matching and alerts.

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• Automating threat feed ingestion
• Standardizing and deduplicating threat indicators
• Distributing indicators to multiple monitoring systems

Build a central vulnerability dashboard with DefectDojo to combine scanner findings, remove duplicates, and track remediation. Includes Docker deployment, automated scan imports from CI/CD pipelines, Jira ticket integration, and API queries for reporting metrics.

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• Combining findings from multiple security scanners
• Importing scan results automatically from CI/CD pipelines
• Connecting vulnerability findings to Jira tickets

Build Splunk pipelines that import threat intelligence and match security events against indicators such as malicious IP addresses, domains, and file hashes. Add source, confidence, and severity details so analysts have context when investigating matches.

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• Importing external threat feeds into Splunk
• Matching network and endpoint logs against threat indicators
• Adding threat and asset context to alerts

Set up and run kube-bench to check Kubernetes configuration against CIS security benchmarks. Select the appropriate benchmark and cluster components, then export JSON or JUnit results for automated pipelines and troubleshoot skipped checks or incorrect detection.

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• Setting up kube-bench for the first time
• Selecting benchmark versions and scan targets
• Exporting JSON or JUnit results for CI

Build and review Bash scripts for automation and CI/CD, the pipelines that test and deliver software. Add input validation, error handling, safe file operations, tests, and platform checks to make script behavior easier to verify.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing error handling and input validation in Bash scripts
• Adding automated shell script tests to CI/CD
• Checking Linux and macOS portability

Build custom cognee processing by chaining Python tasks, defining data types, and saving results to graph and vector databases. Use it to enrich existing data, inspect run status, and troubleshoot how tasks pass data.

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• Building custom extraction and processing steps
• Saving custom data types to graph and vector databases
• Adding information to an existing graph

Manage shell commands and AI agent tasks through a shared gbrain job queue. Track progress, steer agents, pause or resume work, and coordinate parallel tasks, with guidance for long operations, completion reporting, and reusable stage checkpoints.

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• Run scripts as monitored background jobs
• Coordinate parallel research by AI agents
• Redirect, pause, or resume an agent task

Design and operate shared secret storage with HashiCorp Vault or cloud services. The skill guides access policies, automated credential rotation, temporary credentials, audit analysis, and secret delivery to applications and deployment pipelines.

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• Setting up Vault or a cloud secret store
• Automating database password and API key rotation
• Auditing access to secrets

Prepare trained machine learning models for production with deployment, monitoring, and retraining workflows. Build RAG systems that retrieve documents for answers, and integrate language model APIs with retries, fallback providers, and cost tracking.

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• Deploying a trained model to production
• Setting up model monitoring and retraining workflows
• Building a RAG pipeline for document-based answers

Create CI/CD configurations, which automate build, test, and deployment steps, alongside Terraform infrastructure templates and Kubernetes deployment files. Generate health-check and rollback instructions to review before applying changes to cloud infrastructure or application environments.

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• Setting up GitHub Actions or CircleCI pipelines
• Creating Terraform modules for AWS, GCP, or Azure
• Preparing blue/green or rolling deployments

Checks application code, dependencies, and security controls to identify vulnerabilities and plan fixes. Includes Python scanners, reports, incident response workflows, and guidance for adding security checks to CI/CD, the automated build and deployment process.

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• Checking code for exposed secrets and injection risks
• Prioritizing fixes for known dependency vulnerabilities
• Adding security checks to CI/CD pipelines

Inspect project files to identify languages and build tools, then generate starter GitHub Actions or GitLab CI workflows. Set up automated checks, caching, and deployment stages that reflect the project’s commands and target environments.

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• Setting up automated checks for a new project
• Reworking fragile pipeline configuration
• Moving between GitHub Actions and GitLab CI

Audit environment files and repository contents for possible credential leaks, then prioritize findings by severity. Plan credential replacement, improve configuration examples, and establish checks in development and delivery pipelines to reduce accidental exposure.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking configuration files before a commit
• Planning credential replacement after a leak
• Keeping environment examples safe for new contributors