Results for “liquiditaetsplanung”

Coordinate the end-to-end pipeline from CAD or source assets to SimReady USD assets, including conversion, material and physics assignment, validation, conformance, and optional packaging.

Designs ETL/ELT data pipelines with Apache Airflow, dbt, or Prefect configurations

CI/CD pipeline setup — GitHub Actions, testing, linting, deployment. Use when setting up CI from scratch, adding GitHub Actions, automating tests, wiring up deployment, or improving an existing build pipeline.

Expert-level guidance for GitLab CI/CD pipeline configuration, development, optimization, debugging, and best practices.

Designs and implements CI/CD pipelines, infrastructure automation, and workflow optimization to reduce manual tasks.

ML pipeline design — data versioning, experiment tracking, deployment patterns, drift monitoring. Use when building an ML pipeline from data to deployment, setting up MLOps tooling (DVC, MLflow, model registry), choosing deployment patterns (shadow, canary, A/B), or designing monitoring for drift and degradation.

Optimize Kubernetes cluster costs by right-sizing resources, configuring autoscalers, using spot instances, and monitoring spend.

Guides project-level decisions for LLM-powered systems: task-model fit, pipeline architecture, token and cost estimation, and agent-assisted iteration.

Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.

Optimizing FinOps and cloud cost: IaC-based estimation, right-sizing, RI/SP recommendations, anomaly detection, budget alerts, AI/GPU workload economics. Use to forecast or cut cloud spend.

Use this skill for glTF/GLB assets, compression, optimization, transforms, texture sizing, browser delivery. Trigger when the task involves 3d web work related to Gltf Asset Pipeline, production implementation, audits, debugging, strategy, or validation.

Diseña y optimiza pipelines de CI/CD para GitHub Actions y GitLab CI, incluyendo automatización de tests, linting, builds y deploys.

Data pipeline patterns — ETL/ELT, batch vs streaming, idempotency, orchestration. Use when designing a data pipeline, choosing between batch and streaming, implementing ingestion or transformation, setting up orchestration, or debugging pipeline failures.