Results for “ml-supply-chain”

Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking, creates orchestration DAGs, builds feature store schemas, deploys model registries, and automates retraining and validation workflows.

ML pipeline design — data versioning, experiment tracking, deployment patterns, drift monitoring. Use when building an ML pipeline from data to deployment, setting up MLOps tooling (DVC, MLflow, model registry), choosing deployment patterns (shadow, canary, A/B), or designing monitoring for drift and degradation.

Turn model work into a production ML system with data contracts, repeatable training, measurable quality gates, deployable artifacts, and operational monitoring.

生产级机器学习工程工作流——数据契约、可复现训练、模型评估、服务部署、监控与回滚

Design, implement, and validate reproducible machine-learning pipelines spanning data preparation, training, evaluation, registry, and deployment gates. Use when the user requests an ML pipeline, needs to turn model scripts into an orchestrated workflow, or provides pipeline components that must be connected safely.

Debugs ML pipelines by identifying data leakage, overfitting, and distribution shift issues

Guides machine learning development with experiment tracking, hyperparameter optimization, model registry, and MLOps pipeline integration.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks, including model serving, feature engineering, A/B testing, and monitoring.

Manages the machine learning lifecycle with experiment tracking, model versioning, reproducible runs, and deployment through the MLflow platform.

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

Turns model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Manage issues, projects & team workflows in Linear. Use when the user wants to read, create or updates tickets in Linear.

Discover supply-chain manifests, run structured supply-chain audits, and export SBOMs through a dedicated MCP surface.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.

Full daily sales pipeline execution. Run at 9 AM via cron. Scans for signals, enriches company leads, auto-applies to gigs, runs multi-channel outreach, processes follow-ups. All delivery automated via Chrome DevTools MCP.

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.

Verify supply chain integrity for AI agent plugins, tools, and dependencies by generating SHA-256 manifests, detecting tampered files, auditing dependency pinning, and enforcing promotion gates.

Deploy a trained model to serving with versioning, shadow or canary rollout, and a tested rollback path.

Integrate procurement systems with supplier portals, delivery tracking, inventory management, and supply chain analytics for real-time visibility across the supply chain

Manage issues, projects & team workflows in Linear. Use when the user wants to read, create or updates tickets in Linear.

Manage issues, projects & team workflows in Linear. Use when the user wants to read, create or updates tickets in Linear.

Guides the creation of MCP servers that enable LLMs to interact with external services through well-designed tools.

Investigate supply chain attack artifacts including trojanized software updates, compromised build pipelines, and sideloaded dependencies to identify intrusion vectors and scope of compromise.

Entrena modelos de machine learning con Scikit-learn, LightGBM y XGBoost, desde un baseline hasta un modelo productivo con validación robusta y explicabilidad.

Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows

Use when implementing ML functionality with production-grade patterns and safeguards.

Manages a MariaDB database schema across its lifecycle using the MariaDB Schema Management plugin via the mariadb-shell MCP server, covering project creation, versioned development, releases, and deployment.

【麒麟】SKILL.md — 全球电商增长引擎 v5.0

Full token lifecycle from creation through bonding curve trading, graduation detection, AMM migration, fee collection, and volume tracking on Solana using PumpSdk and OnlinePumpSdk.

Manage issues, projects & team workflows in Linear. Use when the user wants to read, create or updates tickets in Linear.

MLOps Engineer IA — Expert en déploiement de modèles ML (A/B testing, feature stores, MLflow, Kubeflow). Industrialise le ML du notebook à la production.

Enforces ML infrastructure, experiment tracking, reproducibility, model packaging, CI/CD, monitoring, and infrastructure-as-code standards at principal-engineer level.