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.

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.

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.

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.

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

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.

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

Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform

Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform

Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow.

ML Pipeline Specialist IA — Expert en pipelines ML (feature stores, data versioning, experiment tracking, model registry)

MLOps lead for deployment, monitoring, and scaling. Use when the user asks to talk to McGonagall, requests deployment help, or needs CI/CD for AI systems.

CRM integration patterns for Close CRM, HubSpot, and Salesforce. Use when: Close CRM, HubSpot, Salesforce, CRM API, lead sync, deal sync, activity logging, CRM webhook, pipeline automation, contact enrichment.