Results for “model-management”

Platform APIs for model management, pricing, and usage tracking

Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check deployment status, verify serving endpoints, or clean up resources by undeploying models and deleting endpoints.

Provides strategic financial frameworks for startup CFOs and finance leaders, including financial modeling, unit economics analysis, fundraising strategy, cash management, and board financial packages.

Adopts the perspective of Naval Ravikant to answer questions using his mental models, decision heuristics, and core concepts like leverage, specific knowledge, and desire management.

Provides guidance for building and modifying content management systems across platforms like WordPress, Shopify, Drupal, and Webflow, covering themes, plugins, admin interfaces, media handling, content models, and static export workflows.

Expert en ComfyUI workflows avancé (install, custom nodes, model management, workflow design)

Creates and manages AI agent teams for complex engineering tasks, with model routing, cost optimization, and performance evaluation.

Automate BigML machine learning operations through Composio's toolkit via Rube MCP, including model creation, training, and deployment.

Provides a framework for rolling out organizational changes using the ADKAR model adapted for startups, with communication templates, resistance patterns, and change fatigue management.

Playwright E2E testing patterns, Page Object Model, configuration, CI/CD integration, artifact management, and flaky test strategies.

Playwright E2E testing patterns, Page Object Model, configuration, CI/CD integration, artifact management, and flaky test strategies.

Manage machine learning models in the Agent Platform Model Registry: list, describe, upload, update, and delete models and their versions.

Manages Agent Platform serving endpoints: create, list, describe, update, delete, and troubleshoot permission, quota, and resource busy errors.

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.

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.

Agent skill for data-ml-model - invoke with $agent-data-ml-model

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

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

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

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

Monitor a live model for data quality, input and prediction drift, performance decay, and fire retraining triggers.

Playwright E2E testing patterns, Page Object Model, configuration, CI/CD integration, artifact management, and flaky test strategies.

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.

Train machine learning models end-to-end, covering data loading, preprocessing, architecture selection, training loops, validation, and checkpointing. Use when the user requests model training or provides relevant inputs for this workflow.

Fine-tune open models or Gemini models using Agent Platform infrastructure, from environment setup through data preparation, job configuration, monitoring, and deployment.

Large Language Model development, training, fine-tuning, and deployment best practices.

Scaling Data-Constrained Language Models

Plan which model tier handles which work BEFORE execution begins — a high-cognition model deeply understands the problem, lays the foundations, then emits a modular plan assigning each module the cheapest tier that can safely execute it, with escalation tripwires and one-way-door protection. Advisory only: it announces "next module → tier X / model Y" at each boundary and the HUMAN switches models — harnesses like Cursor cannot switch mid-run. Load when the user asks which model to use, wants a model plan, model tiers, model-tier routing, assign models to tasks or modules, says "cheap model got stuck", "which model for this task", "cost-efficient model choice", or when implementation-plan / problem-to-plan need a model: tier column. NOT dynamic-routing (plan-path selection after failure) — this skill assigns cognition tiers to work.

Big Data Based Modeling Skill