'scalable' 검색 결과

Plan backend services and APIs, from service responsibilities and interface definitions to authentication, failure handling, monitoring, and rollout. Produces architecture diagrams, API specifications, and documented tradeoffs based on business and operational requirements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing an order management API
• Defining responsibilities across multiple services
• Planning retries and timeouts for external integrations

Design REST and GraphQL APIs by clarifying who will use them and how. Define data structures, errors, authentication, pagination, and versioning, then review examples to make the interface consistent and easier to use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a new REST or GraphQL API
• Reviewing API specifications before implementation
• Defining shared API design standards for a team

Plan backend systems with clear module boundaries and dependency rules. This skill helps select architecture patterns, organize business logic, and outline migration steps for systems that are easier to maintain and test.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a new backend system
• Restructuring a monolithic application
• Defining shared architecture standards

Create patent line drawings from text or reference images through the PatentFig AI API. Convert existing drawings into scalable vector files for CAD, increase image resolution, and export figures in formats used for patent filing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating patent figures from invention descriptions
• Generating drawings based on sketches or photos
• Converting existing drawings into SVG or DXF

Set up model training with torchforge for reinforcement learning, where rewards guide improvement. Covers training configurations, custom reward and loss functions, and distributed execution with Monarch and TorchTitan, keeping algorithm experiments separate from infrastructure concerns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train mathematical reasoning models with GRPO
• Experiment with custom reward and loss functions
• Scale training across multiple GPUs or nodes

Build searches with Qdrant that find similar content and filter results by attributes. The skill covers document retrieval for AI answers, vector storage, memory optimization, and distributed deployment for larger collections.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building document retrieval for AI answers
• Combining similarity search with category or date filters
• Reducing memory use for large vector collections

Evaluate language models with over 100 benchmark tests covering knowledge, math, coding, safety, and image understanding. Configure runs on Docker, Slurm computing clusters, or cloud infrastructure, compare models, and export results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare models using the same benchmark tests
• Run large evaluations on a Slurm cluster
• Evaluate safety and image understanding

Design and implement systems that collect, transform, validate, and store data for analysis. Covers scheduled and real-time processing, data warehouses, quality checks, monitoring, and cost tuning using tools such as Apache Spark, dbt, and Airflow.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building scheduled data collection and transformation workflows
• Designing a data warehouse for analysis
• Synchronizing database changes in real time

Organize PyTorch training code with LightningModule and Trainer. Follow examples for validation, logging, model saving, and distributed training across multiple GPUs, with callbacks that add monitoring or stop training when progress stalls.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Converting an existing PyTorch training loop
• Adding validation, logs, and model checkpoints
• Configuring training across multiple GPUs

Design systems that collect, transform, and store data. Choose between scheduled and continuous processing, generate workflow configurations, validate data quality, and analyze performance using guidance for tools including Python, SQL, Spark, Airflow, dbt, and Kafka.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing data ingestion and transformation pipelines
• Choosing scheduled or real-time data processing
• Checking data freshness, completeness, and uniqueness

Turn system structures, processes, and concepts into dark-themed diagrams. Create architecture diagrams, flowcharts, timelines, and other visual explanations as scalable SVG files, with PNG versions for use as images.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explain system components and their connections
• Map process steps and decision branches
• Show interactions in chronological order

Build image export features for HTML elements with snapDOM. The skill covers SVG, PNG, JPG, and WebP output, preserving styles and fonts, adjusting dimensions, excluding elements, and troubleshooting missing resources.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding image downloads for HTML elements
• Exporting charts and graphics as SVG
• Creating images with specified dimensions

Create pixel art illustrations as SVG files, a scalable image format, for READMEs, documentation, and slides. Build character portraits, conversations, or group scenes, then refine colors, speech bubbles, arrows, and spacing to fit the requested layout.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a pixel art hero image for a README
• Illustrate a conversation with characters and speech bubbles
• Make a character illustration in project colors

Build searches with Qdrant that find similar content and filter results by attributes. The skill covers document retrieval for AI answers, vector storage, memory optimization, and distributed deployment for larger collections.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building document retrieval for AI answers
• Combining similarity search with category or date filters
• Reducing memory use for large vector collections

Evaluate language models on knowledge, math, coding, safety, and image understanding benchmarks. The skill guides reproducible runs in containers using local Docker, Slurm clusters, or cloud execution, with shared configurations for model comparison and result export.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing models on the same benchmark tasks
• Running a small evaluation sample before a full benchmark
• Running large evaluations on a Slurm cluster

Guides reinforcement learning, where models learn from rewards, using torchforge and PyTorch. Covers reward design, custom loss functions, and distributed training so researchers can experiment with algorithms while keeping infrastructure concerns separate.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train mathematical reasoning models with GRPO
• Experiment with custom loss functions
• Scale training across multiple GPUs or nodes

Organize PyTorch training code with LightningModule and Trainer. This skill guides training, validation, logging, model checkpoint saving, early stopping when progress stalls, and configuration for running the same model across multiple GPUs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Organize existing PyTorch training code
• Add validation and metric logging
• Save the best models and stop stalled training

Design and implement a NestJS backend organized into business modules within one deployable application. The skill defines module boundaries, separates data ownership, builds layered code and event communication, and validates isolation through checks and tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a new modular NestJS backend
• Implementing business modules with layered responsibilities
• Connecting modules through events

Add sun shafts and crepuscular rays to a Three.js or equivalent 3D scene, using scene occlusion, a projected sun position, controlled foreground spill, and scalable post-processing. Use for rays of light from the sky, sunlight through trees or roofs, and golden-hour architectural scenes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create scalable programmatic SEO page templates with title patterns, internal linking logic, schema markup, and thin content avoidance strategies. Use when given a niche and data source to build page templates, establishing programmatic SEO structure, or scaling content production with templates. Platform: Google.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.