Results for “scaling”

Scalingo is a European (French) Platform-as-a-Service for deploying and operating web applications, background workers, and managed databases. Use this skill whenever the user mentions Scalingo, wants to deploy an app to Scalingo, work with the scalingo CLI, provision addons (PostgreSQL, MySQL, MongoDB, Redis, OpenSearch, InfluxDB), configure review apps, manage the scalingo.json manifest or a Procfile, scale containers, set up log drains, migrate from Heroku, use the Scalingo Terraform provider, or target sovereign regions (osc-fr1, osc-secnum-fr1 on SecNumCloud infrastructure). Also trigger for "deploy a French PaaS", "HDS-compliant hosting", "SecNumCloud deployment", or questions about Scalingo's Heroku-compatible buildpack model.

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Diagnose failures in GKE HorizontalPodAutoscaler (HPA), which adjusts Pod counts automatically. Investigate missing metrics, unexpected scaling behavior, and slow reactions, then receive proposed configuration fixes or commands for review before deployment.

• Investigating unknown metrics and HPA errors
• Diagnosing workloads that will not scale up or down
• Troubleshooting scaling to and from zero

Set up NVIDIA infrastructure for synthetic data generation on MicroK8s or Azure AKS. Coordinate Kubernetes, inference services, and OSMO workflow execution, with prerequisite checks, deployment validation, capacity planning, and failure investigation before workloads are retried.

• Set up a synthetic data generation environment on MicroK8s
• Deploy an Azure AKS environment with OSMO and inference services
• Check GPU quotas and capacity before scaling

Configure and troubleshoot automatic node scaling in GKE. Examine logs and workload constraints to explain why nodes fail to grow or shrink, and guide tuning for capacity shortages, zone placement, and sudden traffic increases.

• Enable automatic scaling for GKE node pools
• Find why nodes do not scale up
• Identify constraints that prevent nodes from scaling down

Adjust application capacity on GKE through manual scaling, HPA for changing the number of running pods, and VPA for adjusting CPU and memory allocations. Includes configuration templates and a workflow for reviewing resource requests against actual usage.

• Scale application capacity automatically as demand changes
• Review CPU and memory allocations against actual usage
• Change the number of application replicas manually

Deploy SageMaker endpoints, the services that answer model requests, with automatic capacity scaling, CloudWatch alarms, and resource tags. Supports real-time and asynchronous inference, scaling down to zero instances, deployment checks, and resource cleanup.

• Deploying a real-time model endpoint
• Scaling a sparse workload down to zero instances
• Running long inference jobs asynchronously

Use when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS, including Kubernetes clusters, inference endpoint deployment, OSMO deployment, workload submission readiness, and infrastructure failure recovery. Trigger keywords: physical ai infrastructure, resilient scaling, SDG infrastructure, microk8s, azure aks, NVCF deployment, NIM Operator, OSMO deploy, workflow scaling. Don't trigger for: OSMO log summarization or workload-only operations unless infrastructure setup, scaling, validation, or recovery is requested.

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Provides AWS CloudFormation patterns for Auto Scaling including EC2, ECS, and Lambda. Use when creating Auto Scaling groups, launch configurations, launch templates, scaling policies, lifecycle hooks, and predictive scaling. Covers template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and best practices for high availability and cost optimization.

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Create and review Kubernetes configuration files with examples for health checks, access permissions, resource limits, and automatic scaling. Use the included kubectl commands to investigate container crashes, memory shortages, and deployment problems.

• Create Kubernetes deployment configuration
• Review health checks and resource limits
• Investigate repeatedly crashing containers

Deploy and manage virtual servers on Amazon EC2. Covers instance selection, reusable machine images and launch templates, automatic capacity changes, Spot instances, and troubleshooting, with AWS CLI commands and Terraform configuration examples.

• Launch EC2 servers for application hosting
• Create reusable machine images and launch templates
• Configure automatic scaling for changing demand

Plan technology strategy and engineering team growth with frameworks for technical debt, hiring, architecture decisions, and vendor evaluation. Use Python tools and decision templates to prioritize improvements, structure teams, and track engineering performance.

• Prioritizing technical debt reduction
• Planning hiring and engineering team structure
• Comparing technologies and vendors

Turn company strategy into operational plans through process design, OKRs—objectives paired with measurable results—and regular reviews. Produce bottleneck analyses, efficiency scorecards, meeting schedules, and readiness reports for teams preparing to grow.

• Connecting company goals to department and team OKRs
• Finding process bottlenecks and planning improvements
• Assessing operational readiness for team growth

Build a consistent way to run a company by defining responsibilities, weekly metrics, meeting schedules, and quarterly priorities. Compare management frameworks and develop practical routines for resolving issues, tracking commitments, and sharing company information.

• Choose a company management framework
• Clarify who owns each business function
• Design weekly metrics and leadership meetings

Audits arithmetic code for mixed units, decimal precision mistakes, and scaling errors. Adds comments identifying each value’s units and produces a summary of findings, unit definitions, and files reviewed.

• Reviewing token amounts and price conversions in DeFi code
• Checking WAD/RAY scaling errors
• Documenting units and precision in existing formulas

Configure Kubernetes autoscaling with HPA, VPA, and KEDA. Use for horizontal/vertical pod autoscaling, event-driven scaling, and capacity management.

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The enhancement layer for AI-generated content. This skill covers AI-powered visual effects, compositing, upscaling, restoration, and post-production magic—turning raw AI output into polished, professional content. AI generation gets you 80% of the way. Visual effects get you the remaining 20% that separates "clearly AI" from "how did they do that?" This skill covers ComfyUI workflows, Runway's AI tools, intelligent upscaling, rotoscoping, color grading, and the integration of AI elements into traditional footage. The practitioners of this skill are technical artists who understand both traditional VFX workflows and the new AI-native approaches that are revolutionizing post-production. Use when "AI visual effects, VFX, upscale, upscaling, composite, rotoscope, background removal, color grade, inpaint, outpaint, style transfer, enhance, ComfyUI, post-production AI, vfx, visual-effects, compositing, upscaling, post-production, comfyui, enhancement, color-grading" mentioned.

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Manages scaling for GKE workloads using HPA and VPA. Use when configuring Horizontal Pod Autoscaler (HPA), configuring Vertical Pod Autoscaler (VPA), or applying best practices for GKE workload autoscaling. Do not use for cluster-level autoscaling (Cluster Autoscaler), static cluster sizing, or configuring node-level machine styles directly.

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Operations leadership for scaling companies. Process design, OKR execution, operational cadence, and scaling playbooks. Use when designing operations, setting up OKRs, building processes, scaling teams, analyzing bottlenecks, planning operational cadence, or when user mentions COO, operations, process improvement, OKRs, scaling, operational efficiency, or execution.

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Run machine learning workloads on Modal's cloud GPUs without managing servers. Guides Python configuration for training, inference, batch processing, and API deployment, including automatic scaling, persistent storage, scheduled jobs, and troubleshooting.

• Running GPU inference without managing servers
• Deploying a model as an automatically scaling API
• Processing large batches in parallel

Review architecture and engineering growth plans through six questions covering capacity limits, technical debt, staffing, build-versus-buy costs, reliability, and security. Produce a structured verdict and three next steps before committing to major technical decisions.

• Reviewing a major architecture change
• Checking readiness for tenfold traffic growth
• Comparing in-house development with a vendor

Review Google Cloud workloads using the Performance Optimization pillar of the Well-Architected Framework. Assess performance requirements and architecture, then get actionable recommendations for resource allocation, caching, independent components, automatic scaling, and ongoing monitoring.

• Review resource capacity for expected peak demand
• Assess automatic scaling for traffic spikes
• Review component design for independent scaling

Assess whether a GKE cluster or application is ready for production. Coordinates specialist skills to review scaling, security, reliability, monitoring, recovery, costs, and maintenance, then reports domain status, a weighted readiness score, and remediation priorities.

• Review a GKE application before its production launch
• Assess production readiness across an entire cluster
• Prioritize fixes using domain status and a readiness score

Design production GKE clusters, which run containerized applications on Google Cloud, using recommended Autopilot settings. Review networking, security, monitoring, and upgrade choices, then compare existing clusters against the configuration and identify deviations and remedies.

• Design a production GKE cluster
• Review network choices before cluster creation
• Audit cluster settings against the recommended configuration

Run machine learning workloads on Modal's cloud GPUs without managing servers. Guides Python configuration for training, inference, batch processing, and API deployment, including automatic scaling, persistent storage, scheduled jobs, and troubleshooting.

• Running GPU inference without managing servers
• Deploying a model as an automatically scaling API
• Processing large batches in parallel