'processing' 검색 결과

Java의 Quarkus 서비스 설계와 검토를 지원합니다. REST API 구성, Panache 데이터 접근, Camel 메시지 연계와 비동기 처리의 구현 패턴을 제공합니다.

• REST API의 책임 분리
• Camel과 RabbitMQ 연계
• Panache 저장 처리 검토

PDF 분석, 텍스트 추출, 이미지 분석 결과를 파일 내용에 따라 저장하고 재사용하도록 설계합니다. 파일을 옮기거나 이름을 바꿔도 재사용하며 내용이 바뀌면 다시 처리합니다.

• PDF 텍스트 추출 결과를 재사용합니다
• 이동한 파일의 처리 결과를 재사용합니다
• 기존 처리 함수에 캐시를 추가합니다

Submit groups of AI requests as batch jobs with the OmniRoute command line. Manage input files, monitor or cancel jobs, save results and errors, and incorporate batch processing into automated build and deployment workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Submitting AI requests from a JSONL file
• Monitoring batch jobs and waiting for completion
• Saving batch results and error details

Replace a face in a video using a source photo and the HeyGen API. The skill covers submitting image and video URLs, checking processing status, and retrieving the finished video, including workflows that personalize generated avatar videos.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Replace a face in a recorded video
• Personalize an avatar video with a face photo
• Track processing and retrieve the finished video

Manage Stripe customers, products, invoices, and recurring subscriptions through its official MCP server, a connection for agent tools. Handle billing changes, payment links, duplicate-charge refunds, disputes, balances, payouts, and processing-fee reconciliation using reviewed operations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Manage recurring subscriptions and billing-plan changes
• Handle invoices and payment links
• Refund duplicate charges and address payment disputes

Build Python workflows for filtering, grouping, joining, and reshaping table data with Polars. Get guidance on migrating pandas code and using lazy evaluation, which optimizes a processing plan before execution, for large datasets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Filtering and summarizing large CSV files
• Migrating pandas processing code to Polars
• Joining and reshaping multiple tables

Build Python machine learning workflows for classification, numerical prediction, and grouping similar data. Combine preprocessing with training, compare algorithms, tune model settings, and evaluate performance using data splits suited to the task.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building classification or numerical prediction models
• Grouping similar records and visualizing the results
• Combining missing-value handling with model training

Use PathML for local pathology image research, from loading whole-slide images and splitting them into tiles to preprocessing and quality checks. Plan multiplex measurements, analyze spatial relationships between cells, and run model inference in bounded batches.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning tile extraction from whole-slide images
• Reviewing preprocessing and image quality
• Validating multiplex measurements and spatial graphs

Validate particle data and run RELION refinement for single-particle cryo-electron microscopy. Check independently refined half maps, calculate diagnostic map correlations, and perform soft-mask postprocessing while preserving execution logs and restart information.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check particle metadata and image-stack consistency
• Run refinement on a selected particle population
• Inspect agreement between independent half maps

Helps build and review NeuroKit2 research workflows for physiological recordings such as ECG, respiration, and skin conductance. Covers preprocessing, time alignment, event analysis, heart rate variability, and complexity measures, while recording methods and checking data quality.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspect physiological recordings before preprocessing
• Analyze ECG and heart rate variability for research
• Plan event windows and baseline correction

Optimize scientific Python workloads for NVIDIA GPUs by choosing suitable libraries and reducing bottlenecks. Compare outputs with the CPU version and measure complete processing time, including data transfers, to determine whether the GPU implementation provides a useful improvement.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess whether slow Python processing would benefit from a GPU
• Move NumPy or pandas workloads to suitable GPU libraries
• Validate GPU results against CPU outputs

Create automated workflows that add threat intelligence to suspicious IP addresses, domains, URLs, and file hashes. Combine findings from multiple services into consistent alert context and confidence scores to support analysts reviewing security alerts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding threat context to security alerts
• Processing indicators from phishing email submissions
• Standardizing bulk indicator lookups from threat feeds

Guides background processing in Node.js and TypeScript with BullMQ and Redis. Covers job queues, which hold tasks for later execution, along with scheduling, priorities, rate limits, worker concurrency, and dependencies between processing steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design a queue for background tasks
• Schedule jobs for later or repeated execution
• Organize dependent steps into a job flow

Turn audio and video into text with Whisper, including Japanese and other supported languages. This skill covers transcription, English translation, timestamped subtitles, model selection, and batch processing, with guidance on hardware needs and recognition limitations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Transcribing recorded meetings
• Creating subtitles for videos
• Translating multilingual recordings into English text

Extend the amount of text a language model can process at once. Compares RoPE, YaRN, ALiBi, and position interpolation, with implementation examples, additional training guidance, and memory optimization approaches for long document processing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extend an existing model to handle longer inputs
• Compare methods for representing token positions
• Plan additional training with long documents

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Export data from Google Sheets as CSV for local backups or further processing. The recipe covers checking spreadsheet details, exporting through Google Drive, and reading a specified sheet range directly in CSV format.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Keeping a local CSV backup of spreadsheet data
• Exporting spreadsheet data for further processing
• Reading a selected sheet range as CSV

Plan a Zoom meeting bot or recorder, including how it joins meetings, handles live audio, video, and transcripts, and connects backend services for storage, post-processing, and event handling.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a bot that joins Zoom meetings
• Planning live audio, video, and transcript processing
• Organizing post-meeting storage and retries

Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Aggregating and deduplicating multiple records
• Transforming complex API responses
• Fixing data access and return format errors

Build Gemini API integrations on Google Cloud’s Agent Platform using the Google Gen AI SDK. Get guidance on authentication, text and media processing, structured JSON responses, function calls, caching, batch processing, and real-time voice or video interactions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding Gemini to enterprise applications
• Processing images, audio, and documents
• Generating JSON in a specified format

Run batch processing, scientific simulations, and machine learning training on Google Kubernetes Engine. Configure parallel jobs, queues that manage resource access, and distributed computation, with guidance on scaling idle capacity down and recovering from interruptions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run data processing jobs in parallel
• Share computing resources through job queues
• Configure distributed simulations with MPI

Deploy and manage applications on Cloud Run using source code or container images. Choose services for web requests, jobs for tasks that finish, or worker pools for continuous background processing, with guidance on execution settings and deployment failures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploy a web application from source code or a container
• Create and run batch jobs with parallel tasks
• Deploy continuous queue-processing workers

Receive live Lark/Feishu updates for messages, approvals, tasks, meetings, and more. Stream them as NDJSON, one JSON record per line, with filtering and time or event limits for bots and automated event processing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Receiving new messages for bot processing
• Monitoring approval and task updates
• Receiving meeting lifecycle and minutes events

Turn recorded audio and video into text or timestamped subtitles with Whisper. This skill covers transcription across 99 languages, language detection, translation into English, batch processing, and model selection for available computing resources.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Transcribe recorded meetings
• Turn podcasts into text
• Generate SRT or VTT subtitles for videos