「serving」の検索結果

UMAP dimensionality reduction for visualization, clustering prep, and feature engineering. Fast nonlinear manifold learning preserving local and global structure. Standard UMAP (fit/transform, sklearn-compatible), supervised/semi-supervised, Parametric UMAP (NN encoder/decoder, TensorFlow), DensMAP (density), AlignedUMAP (temporal/batch). 15+ distance metrics, custom Numba metrics, precomputed distances. For linear reduction use PCA; for neighborhood graphs use sklearn NearestNeighbors.

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Batch-resize or convert a folder of images for web, social media, slides, or ARCH-size print output while preserving originals. Use for image resizing, format presets, or photo batches; not for product-image background removal.

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Python backend for Databricks Apps — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex. **Default for a new Databricks App is `databricks-apps` (AppKit — Node/TypeScript/React) — reach for it first.** Use this skill only when the user asks for a Python backend, extends an existing Python app, or the team is Python-only. Covers OAuth auth, app resources, SQL warehouse and Lakebase connectivity, foundation-model / Vector Search / model-serving APIs (via `databricks-python-sdk`), and deployment via CLI or DABs.

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Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-backed), custom PyFunc, custom ResponsesAgent (LangGraph + UC Function/Vector Search); UC feature tables + FeatureLookup + point-in-time joins + Lakebase online store; declarative Feature Views (create_feature, DeltaTableSource, RollingWindow/SlidingWindow/TumblingWindow, materialize_features, streaming Kafka features). NOT for: endpoint ops (databricks-model-serving), MLflow evaluation (databricks-mlflow-evaluation).

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Use when building, auditing or freezing a journal submission package from the canonical manuscript. Detects drift between the source and the per-journal submission copy, builds byte-preserving packages with manifests, and records current, stale or frozen status.

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Use when a manuscript or response-to-reviewers letter reads as AI-written. Scans for 27 AI writing patterns and rewrites flagged passages, preserving technical accuracy and bounding how much text changes. Not general copy-editing; that is /polish-language.

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Guides FastVideo video, image, and audio generation, config-first inference, serving, training, distillation, evaluation, and performance workflows.

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Use FedML for setup, CLI operations, MLOps launch, distributed and federated training, model serving, and workflow orchestration.

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Use for Feast feature store tasks: feature repositories, definitions, CLI, retrieval, materialization, serving, RAG/vector search, integrations, and Feast contributor workflows.

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Use this repo skill for DiscoArt image generation, configuration/prompt scheduling, CLI, Jina serving, Docker runtime planning, and troubleshooting.

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Guides Earth2Studio weather and climate inference workflows, data access, model and component selection, output/checkpoint handling, extension, and optional REST serving with backend-aware validation.

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Route DataFlow workflows for pipelines, text and document processing, serving, evaluation, and Ray acceleration.

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Use DeepFace for face recognition, verification, embeddings, face detection, demographic analysis, datastore search, API serving, model selection, and troubleshooting.

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DeepPavlov package routing for config-driven NLP workflows, model-family selection, and serving.

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Guides ChatGLM2-6B Transformers inference, local demos, FastAPI/OpenAI-compatible serving, C-Eval evaluation, and P-Tuning v2 fine-tuning.

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Use Chonkie for text/document chunking, pipelines, embeddings, CLI/API serving, and storage integrations.

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Routes Chinese-LLaMA-Alpaca-2 training, inference, serving, evaluation, and integration workflows.

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Operate ASRT SpeechRecognition Chinese ASR data, acoustic models, pinyin language model, and serving clients.

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Use BentoML to author model-serving Services, build and containerize Bentos, run HTTP/gRPC servers and clients, manage models, operate the CLI and BentoCloud, and configure observability or production runtime behavior. Use when tasks mention BentoML, bentoml service.py, bentofile.yaml, Bento build/serve/deploy, BentoCloud, or BentoML model stores.

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Operate the Align-Anything multimodal alignment package, including training, serving, remote reward models, and satellite projects.

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Create, edit, validate, and run Agent QA tests, suites, and hooks through MCP or CLI while preserving canonical IDs and schema contracts.

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Implement privacy-preserving record linkage across datasets using Bloom filter encoding, secure hash matching, threshold tuning for precision and recall, and false positive management. Enables entity resolution without exposing raw personally identifiable information between parties.

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Implementation guide for secure multi-party computation enabling privacy-preserving analytics across organizations. Covers secret sharing, garbled circuits, reference frameworks MP-SPDZ and CrypTen, practical deployment patterns, and GDPR alignment for joint controller analytics without revealing individual party inputs.

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Architecture guide for GDPR-compliant federated learning systems. Covers horizontal and vertical FL, aggregation strategies (FedAvg, FedProx), communication efficiency, secure aggregation, and differential privacy integration. Includes privacy guarantees analysis and deployment patterns for cross-organizational ML without data sharing.

日本語の概要は準備中です。原文の説明を表示しています。