「data pipeline」の検索結果

Manages Fabric Variable Library items, including definitions, libraryVariables and valueSets overrides. Covers which consumers can reference a variable and with what syntax -- pipelines, notebooks via notebookutils variableLibrary, Dataflow Gen2, Copy Jobs, shortcuts, UDFs and Plan items -- the active value set per workspace or deployment stage, and Git serialization for CI/CD.

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。

Creates and inspects Fabric Activator (Reflex) alerts: rules, their data sources, conditions, and the Teams or email action they fire, plus decoding an existing ReflexEntities definition. Choose it whenever someone wants to be told, or something done, when values cross a threshold. The pipeline delivering those values is eventstream-cli.

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。

Converts Synapse workspaces to Fabric: Spark items, Lake Databases, Linked Services, and Dedicated SQL Pool schema/procedures for Lakehouse or Warehouse. During Pool-to-Lakehouse migration only, can migrate approved dependent procedure callers after notebook readiness. Excludes standalone pipelines and Warehouse administration. Triggers: Synapse workspace to Fabric, Dedicated SQL Pool to Lakehouse, Dedicated SQL Pool to Fabric Warehouse.

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。

Plans and builds end-to-end Fabric data platforms on the Bronze, Silver, and Gold medallion pattern: ingestion to analytics, PySpark and Delta layers, pipeline orchestration, per-layer Spark tuning, Materialized Lake View versus notebook choices for Silver and Gold, and Direct Lake handoff. Use for layered architecture design, planning and recommendations. For questions over an existing Power BI report use fabriciq.

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。

AI sales coach composite. Analyzes all available sales data — email campaigns, call recordings/transcripts, reply patterns, pipeline outcomes — to identify what the user does well, where they struggle, and how to improve. Finds patterns in top-performing emails, winning call techniques, successful objection handles, and deal progression. Produces personalized coaching recommendations based on their specific product, market, and selling style. Tool-agnostic.

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。

Qualifies inbound leads against full ICP criteria — company size, industry, use case fit, role/seniority of the person. Checks CRM and existing customer base for duplicates and existing relationships. Outputs a scored CSV with qualification status, reasoning, and pipeline overlap flags. Tool-agnostic — works with any CRM, enrichment tool, or data source.

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。

Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.

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。

Standardizes molecular structures using the ChEMBL structure pipeline for normalization and parent selection plus RDKit rdMolStandardize for explicit custom steps such as tautomer canonicalization, salt/solvent stripping, charge handling, stereochemistry handling, mixture selection, and isotope normalization. Explicitly compares ChEMBL, canSARchem, RDKit, and PubChem standardization choices. Use when preparing libraries for QSAR training, joining datasets across sources, deduplicating compound collections, or building canonical compound registries.

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 machine vision inspection systems with MATLAB Visual Inspection Toolbox. Covers the full arc from problem selection through deployment: anomaly detection (Student-Teacher, PatchCore, FastFlow, FCDD), object detection (YOLOX), counting (CounTR, shape matching), gauging and measurement (caliper, fitcircle, fitangle), blob analysis (regionprops, calibrated measurement), shape matching for fixturing and localization, synthetic data generation for training augmentation, and deployment to standalone apps, embedded C/C++, NVIDIA GPUs (TensorRT), NPUs (ONNX), and Beckhoff PLCs. Routes to rule-based tools when problems are geometric and deterministic, AI-based tools when defect appearance varies, and hybrid pipelines combining both.

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。

Expert guidance for the Fabric CLI (`fab`) and the Fabric and Power BI REST APIs: workspaces, items, lakehouses, notebooks, pipelines, semantic models, reports, capacities, OneLake, deployment and admin. Also estimates the capacity units (CU) an operation will consume and its impact on the capacity before running it. Automatically invoke whenever the user mentions Fabric, Power BI Service, a Fabric or Power BI workspace, a capacity or F SKU, OneLake, `fab`, or asks to create, run, refresh, schedule, deploy or delete anything in Fabric, including preview items such as Plan, Ontology, Graph or Copilot.

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。

Design RAG pipelines: chunking, retrieval evaluation, and architecture. Use when building a RAG system, selecting a chunking strategy, choosing a vector database, optimizing retrieval quality, or evaluating with RAGAS metrics.

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。

When the user wants help with revenue operations, lead lifecycle management, or marketing-to-sales handoff processes. Also use when the user mentions 'RevOps,' 'revenue operations,' 'lead scoring,' 'lead routing,' 'MQL,' 'SQL,' 'pipeline stages,' 'deal desk,' 'CRM automation,' 'marketing-to-sales handoff,' or 'data hygiene.' For cold outreach emails, see cold-email. For email drip campaigns, see email-sequence. For pricing decisions, see pricing-strategy.

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。

Generates infrastructure for exporting app content to social platforms (Instagram Stories, TikTok, Twitter/X) with platform-specific formatting, aspect ratios, and metadata. Use when user wants social media export, share to stories, or platform-specific sharing pipelines.

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。

The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, an

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。

Manage HubSpot CRM contacts, companies, deals, and CMS content via API. Use when the user says "add contact to HubSpot", "create deal", "search CRM", "HubSpot contacts", "update deal stage", "CRM report", "HubSpot pages", or asks about managing their sales pipeline, CRM data, or HubSpot content.

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 the next period ADS plan must be built from last period results — read the report and the audit, keep and scale winners, stop or fix losers, set new test hypotheses, split budget, plan three budget scenarios, and schedule the creative pipeline. Trigger on 'next month ads plan', 'plan ads from the report', 'what should we run next month', 'Q4 ads plan', 'next quarter media plan', 'the month ended, now what'. Also use when a report and an audit both exist and no decisions have been made. Not for — the first media plan before any data, see `54-media-plan-global`; scoring the account, see `21-ads-audit-global`; the next period CONTENT plan, see `40-next-content-plan-global`.

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。

将用户输入的整数减去5并返回结果。当用户提到「流水线」关键词时并提供数字时使用，并且此方法是「流水线」系列技能之一，能与其他技能之间传递结果。

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。

将用户输入的整数加10并返回结果。当用户提到「流水线」关键词时并提供数字时使用，并且此方法是「流水线」系列技能之一，能与其他技能之间传递结果。

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。

将用户输入的数字乘以10并返回结果。当用户提到「流水线」关键词时并提供数字时使用，并且此方法是「流水线」系列技能之一，能与其他技能之间传递结果。

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。

Retrieve arXiv paper metadata with keyword queries or import an offline arXiv export, and save results as JSONL (`papers/papers_raw.jsonl`).

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。

Multi-route literature expansion + metadata normalization for evidence-first surveys.

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。

Write, review, or modernize F# code in .NET repositories with functional-first design, algebraic data types, pattern matching, pipelines, async workflows, project ordering, and C# interop. USE FOR: F# .fs/.fsproj code; discriminated unions, records, options, results, pattern matching, computation expressions, or functional domain modeling; strongly typed AI-generated .NET code. DO NOT USE FOR: C# language modernization; FSI-only exploratory scripts with no project changes. INVOKES: inspect .fsproj file order and SDK settings, edit F# source and project files, and run dotnet build/test or dotnet fsi validation commands when changes are made.

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 technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).

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 ML.NET to train, evaluate, or integrate machine-learning models into .NET applications with realistic data preparation, inference, and deployment expectations. USE FOR: ML.NET integration; local model training or retraining; inference pipelines, model loading, evaluation, and deployment review. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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。