云、CI/CD、监控、部署（6846 个）
Create container-based hosted agents in Azure AI Foundry using Python and the Azure AI Projects SDK. Follow examples for resource settings, authentication, version management, and checks needed before treating a deployment as healthy.
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Design scheduled AI workflows for document ingestion, batch processing, and model training using Prefect, Airflow, or Dagster. Compare orchestration tools and plan task dependencies, retries, caching, and monitoring through implementation fragments.
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Develop incident response procedures for LLM outages, declining answer quality, spending spikes, and unsafe outputs. Use example monitoring alerts, severity levels, recovery playbooks, and postmortem guidance to organize detection, response, and follow-up work.
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Design traffic controls for AI inference services using Istio configuration examples. Covers mutual authentication, model version routing, gradual releases, retry limits, failure containment, and monitoring, with guidance for validating changes on the target cluster.
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Add monitoring to AI agents with response times, token usage, estimated costs, and error metrics. Trace records follow model and tool calls step by step, helping teams investigate failures and understand operating costs.
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Guide reviews of Linux Kernel patches for memory safety, Terraform plans for costly resource changes, and Kubernetes configurations for restricted permissions. A separately installed tool and configured AI model produce advisory findings and candidate manifests for validation.
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Prepare the next Prisma 8 release by updating workspace versions, assembling upgrade guides, and drafting release notes. Open release and documentation pull requests for maintainer review; merging the release request triggers automated publication.
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Check release readiness inside the herdr repository by comparing changes since the base release with upcoming release notes and documentation. Review CLI descriptions, issue references, checks, and benchmarks, then report readiness.
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Manage Sub2API through its administrator API using a bundled command-line tool. Inspect and update accounts, manage redeem codes, groups, and proxies, import or export accounts, and verify changes with follow-up reads.
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Automate page interactions and screenshots with agent-browser running in AWS Bedrock AgentCore cloud browsers. Use standard browser commands, preserve login state across sessions with persistent profiles, and watch active sessions through an AWS Console link.
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Add browser automation to Vercel apps by running agent-browser and headless Chrome in temporary, isolated virtual machines. Capture screenshots, inspect page structure, and complete multi-step forms while preserving browser state between commands.
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Access and test protected Vercel deployments with agent-browser using an existing Vercel identity and a short-lived authentication token. Keep deployment protection enabled while diagnosing login redirects, environment access mismatches, and application authentication errors.
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Configure Prometheus to collect and store operational metrics from infrastructure and applications. Covers collection schedules, alert rules, automatic discovery of monitored services, and troubleshooting checks to help establish and maintain monitoring.
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Add structured logs, performance metrics, and traces that follow requests across Python services. Establish consistent log fields and request IDs so production failures can be investigated using recorded activity and service health signals.
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Set up distributed tracing with Jaeger and Tempo to follow requests across services. Examine delays, service dependencies, and error propagation, with guidance on sampling, linking logs to traces, and troubleshooting missing trace data.
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Define service reliability measurements and targets, then turn them into Prometheus monitoring rules and alerts. Calculate error budgets, the amount of failure allowed, and outline Grafana dashboards for tracking reliability and deciding when to prioritize fixes.
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Build Grafana dashboards that show application and infrastructure health through request rates, errors, response times, and resource usage. Organize metrics into useful panels, add filters and alerts, and manage dashboard definitions as code.
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Investigate active macOS connection failures without changing network settings. Uses built-in tools to distinguish local network, DNS name lookup, upstream path, and application problems, then reports evidence, confidence, and the next useful diagnostic check.
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Set up Kubernetes deployments with ArgoCD or Flux CD using GitOps, an approach that stores the desired system configuration in Git. Configure automatic synchronization, staged rollouts, secret management, and deployment workflows across environments or clusters.
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Generate Kubernetes YAML files that define how applications run, connect, receive configuration, and use persistent storage. Include resource limits, health checks, security settings, labels, and validation guidance for production-oriented deployments.
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Configure Turborepo builds for projects containing multiple apps and shared packages. Set task dependencies, reuse saved build outputs locally or remotely, limit runs to affected packages, and investigate why cached results are not being reused.
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Create incident response runbooks with ordered recovery steps, severity levels, escalation contacts, and status update templates. Help on-call engineers follow clear procedures during outages, verify recovery, and keep stakeholders informed.
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Write incident review documents that focus on system conditions and learning. Organize timelines, examine root causes with evidence, and turn findings into practical follow-up actions with owners and deadlines, supported by a meeting facilitation guide.
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Prepare handoffs between engineers responsible for responding to system incidents. Organize active incidents, investigations, recent changes, and upcoming events into shift summaries, with guidance for mid-incident transfers, asynchronous updates, and checks that the incoming engineer receives alerts.
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