「classification」の検索結果

Build n8n pipeline for automated cost estimation from Revit/IFC using DDC CWICR database and LLM classification.

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。

Automated cost estimation from BIM models using DDC CWICR database (8 national bases, 78,228 positions). AI classification + vector search for accurate pricing.

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。

Browse and search the OpenConstructionERP cost database: classification tree, SQL and semantic search, autocomplete, certainty badges, and the resource catalog. Use when a user wants to explore cost data without building a BOQ yet.

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。

Automatically classify and extract information from construction documents using NLP. Categorize RFIs, submittals, change orders, specifications, and contracts.

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。

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).

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。

Provides error classification, recovery, and graceful-degradation patterns. Use when implementing error handling or debugging resilience failures in any skill.

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。

Production incident response automation. Reads logs, checks recent deploys, identifies root cause, suggests fixes, drafts incident comms, creates post-mortem templates. Severity classification (SEV1-4), escalation paths, status page updates. Generates incident-report.md with timeline, root cause, impact assessment, remediation steps, and prevention measures.

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。

Wire a System One model (Jev, or an open reproduction like Von) into a product feature — routing, guardrails, scoring, classification. Use when replacing an LLM call that returns a label rather than prose, when adding a typed decision to an agent loop, or when deciding between the hosted Jev API and a local open model. Covers question design, the eval-set-first workflow, threshold calibration, confidence gates, and the traps measured on real data.

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。

Build and run a labelled eval set for a System One model (Jev, Von, or any typed-decision config), then sweep criteria wordings and thresholds against it. Use when a Jev/Von classification is wrong or unreliable, when choosing between the hosted API and a local open model, when tuning noul thresholds, or before shipping any typed-decision feature. Produces an accuracy-by-wording matrix and a calibrated threshold.

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。

Audit a codebase for LLM calls that are really classifications in disguise, then produce a costed swap plan for a System One model. Use when asked to cut AI inference cost or latency, when scoping a performance engagement for a client, when reviewing an agent loop that feels slow, or when asked "where could we use Jev here". Produces a ranked table of candidates with measured latency and dollar deltas.

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。

Post-mortem analysis when a client churns. Takes client history, engagement data, support tickets, usage logs, and exit feedback to produce a comprehensive churn autopsy with root cause classification, timeline of decline, and preventive measures.

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。

Use when you need a runnable PyTorch training repo for a medical-imaging task (segmentation, classification, detection, synthesis, self-supervised, or fine-tuning a pretrained backbone). Emits a patient-level seed-locked split, train/evaluate scripts, config and a Methods stub.

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。

Use when analyzing a researcher's publication record from PubMed. Fetches an author's papers, classifies study types and author position, charts the patterns and writes a strategy report, with an optional trajectory-archetype classification. Works from PubMed metadata only.

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。

Use davidsandberg/facenet for TensorFlow 1.x face recognition workflows: MTCNN alignment, embeddings, classification, LFW evaluation, training, and model export.

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。

Use the DeepGCNs PyTorch operating guide for graph convolution layers, dynamic and dilated KNN blocks, point-cloud classification or segmentation, PPI, OGB/DeeperGCN, and reversible memory-efficient GNN workflows.

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。

Routes tasks for discovering NLP datasets in the CLUEDatasetSearch catalogue, including Chinese NLP classification, QA, matching, NER, generation, translation, corpus, and reading-comprehension resources.

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。

Routes CS230 code-example requests to the correct PyTorch or TensorFlow vision and NLP workflows for SIGNS image classification and named-entity recognition.

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。

Guides Attention-Gated Networks PyTorch medical imaging workflows for ultrasound classification, attention-gated U-Net segmentation, data layout, CUDA setup, and visualization helpers.

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。

On-site and remote vendor audit procedures per GDPR Article 28(3)(h). Covers audit planning, evidence collection methodologies, finding classification, remediation tracking, and audit report generation for processor compliance verification.

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。

Guides privacy law change monitoring and impact assessment for multi-jurisdiction organisations. Covers regulatory tracking sources, change classification, impact scoring methodology, and implementation prioritisation. Keywords: law monitoring, regulatory tracking, change management, impact assessment, implementation priority.

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。

Guides internal privacy audit program design and execution including risk-based audit planning, scope definition, fieldwork procedures, finding classification, evidence gathering, remediation tracking, and management reporting. Covers audit universe definition, annual audit plan, working papers, and closure verification. Keywords: internal audit, privacy audit, fieldwork, remediation, findings, audit plan.

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。

Conducts comprehensive inventory of protected health information across the enterprise per HIPAA Security Rule requirements at 45 CFR §164.308(a)(1)(ii)(A) and §164.310(d). Covers identification of all ePHI repositories, data flow mapping, classification of PHI by sensitivity, and integration with risk analysis. Keywords: PHI inventory, ePHI, data mapping, information asset, data flow, HIPAA risk analysis, designated record set.

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。

Handles GDPR Art. 10 criminal conviction and offence data classification including official authority requirements, national law derogations, and comprehensive register restrictions. Covers controller obligations for criminal background checks and offence records. Keywords: criminal data, Art 10, conviction data, offence records, criminal background, DBS check.

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。

Auditing cookie lifetimes against regulatory recommendations and browser policies. Covers CNIL 13-month maximum recommendation, session vs persistent classification, third-party cookie phase-out impact, and Safari ITP duration caps.

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。