「mlops」の検索結果

データ準備からモデル学習、検証、本番デプロイメントまでのエンドツーエンドMLOpsパイプラインを構築します。MLパイプラインの作成、MLOpsプラクティスの実装、またはモデル学習とデプロイメントワークフローの自動化時に使用します。

Think and work like an expert MLOps Engineer. Use when a task calls for MLOps Engineer judgment. Reasons from data contracts, feature parity, evaluation gates, and rollback-readiness through MLflow/W&B registries, Feast feature stores, KServe/Triton serving, Great Expectations/TFDV validation, and Evidently PSI/KS drift monitors while treating train-serve skew, data leakage, silent degradation, and schema/concept drift as first-class failure modes.

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 end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment 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。

Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform

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。

ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training.

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。

Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform

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。

Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform

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。

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.

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 end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment 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。

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment 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。

Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.

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 user asks to "design an experiment", "build a predictive model", "run A/B test analysis", "perform causal inference", "engineer features", "evaluate model performance", "set up MLOps pipeline", "analyze time series", "calculate sample size", or "deploy a model to production". Expert data science covering statistical modeling, experimentation, causal inference, feature engineering, ML deployment, and advanced analytics with Python, R, and SQL.

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。

ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization.

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。

MLOps across model deployment, ML pipelines, monitoring, and feature stores. Use when deploying models to production, building training pipelines, setting up drift detection, configuring feature stores, or automating ML CI/CD 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。

Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.

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 end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment 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。

MLOps and the production ML lifecycle -- model packaging and serving, CI/CD for ML, experiment tracking, model registries, reproducibility, production monitoring for data and concept drift, retraining pipelines, A/B and shadow deployment, and rollback. Covers batch vs online/real-time inference, REST endpoints, feature stores, data and version pinning, deterministic pipelines, performance-decay detection, and retraining triggers. Use when deploying models to production, serving predictions, monitoring for data or concept drift, setting up ML CI/CD, tracking experiments, managing a model registry, or planning retraining, shadow rollout, and rollback.

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。

Simulate supply chain and adversarial machine learning attacks by injecting poisoned data or targeted backdoors into training and fine-tuning datasets. Use this skill when assessing the integrity controls of MLOps pipelines or evaluating the resilience of AI models against highly targeted, stealthy manipulation intended to alter model behavior on specific triggers.

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。

Rastreie experimentos de ML com logging automático, visualize treinamento em tempo real, otimize hiperparâmetros com sweeps e gerencie registro de modelos com W&B - plataforma colaborativa de MLOps

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 end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment 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。

Data scientist v3 — ML, deep learning, NLP, CV, experimentation, MLOps

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 comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools.

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基础设施实战——从GPU集群调度到模型推理部署的全栈实操指南。覆盖：昇腾/GPU统一调度、PyTorch分布式训练、vLLM推理部署、模型量化实战、LLMOps、GPU故障诊断、HuggingFace生态、MLOps流水线。触发词：AI基础设施、GPU、昇腾、训练、推理、vLLM、PyTorch分布式、模型部署、LLMOps、MLOps、集群调度、HuggingFace

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 comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.

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。