Build and ship ML on Domino
Work with the Domino Data Lab platform from your AI and Codex. Skills cover workspaces, jobs, compute environments, datasets, NetApp volumes, projects, apps, model endpoints and monitoring, MLflow experiment tracking, GenAI tracing, Flows, Spark/Ray/Dask, AI Gateway, governance, tags and properties, and the Domino Python and data SDKs. More
Skills 30 in the ChatGPT listing
Domino ai gateway Access external LLM providers through Domino AI Gateway - a secure proxy with centralized API key management, usage monitoring, and compliance. Domino api intro Introduction to calling Domino platform APIs. Domino app init Scaffold a new Domino-ready web application (Vite + React, Streamlit, Dash, Flask, or Gradio) with proxy-compatible settings, port 8888 binding, and an app.sh entry point. Domino apps Deploy web applications to Domino Data Lab with expertise in React apps (Vite) behind Domino's reverse proxy. Domino data connectivity Connect Domino workloads to external data sources including AWS S3 (via Mountpoint CSI driver), credential propagation with AWS IRSA and Azure Entra ID, and External Data Volumes. Domino data sdk Use the domino-data Python SDK (dominodatalab-data) for programmatic data access in Domino. Domino datasets Work with Domino Datasets - high-performance, versioned filesystem storage. Domino debug proxy Diagnose and fix Domino reverse-proxy and routing problems in published web apps by checking vite.config.js, package.json, and app.sh for wrong ports, localhost binding, absolute base paths, missing SPA fallback, or missing build steps. Domino distributed computing Work with distributed computing frameworks in Domino including Apache Spark, Ray, and Dask clusters. Domino environments Create and customize Domino Compute Environments - Docker containers defining tools, packages, and configurations. Domino experiment setup Set up MLflow experiment tracking in a Domino project by generating an experiment_setup.py helper with deployment-unique experiment names, Domino context tags, and framework auto-logging, plus an example training script. Domino experiment tracking Track traditional ML experiments in Domino using the MLflow-based Experiment Manager. Domino extensions Build and operate Domino UI Extensions (mount Domino Apps in the shell with page context). Domino flows Orchestrate multi-step ML workflows using Domino Flows (built on Flyte). Domino genai tracing Trace and evaluate GenAI applications including LLM calls, agents, RAG pipelines, and multi-step AI systems in Domino. Domino governance Manage model risk governance in Domino using policies, bundles, and evidence. Domino jobs Create, run, and manage Domino Jobs - batch executions for scripts, training, and data processing. Domino launchers Create Domino Launchers - parameterized web forms for self-service job execution. Domino model endpoints Deploy and monitor model API endpoints in Domino. Domino model monitoring Monitor deployed models in Domino including drift detection, model quality tracking, and alerting. Domino model serving Deploy, invoke, and retire Domino model APIs and registered models via REST. Domino modeling assistant Enable AI-assisted model development within Domino by writing needed model and training code and using MCP (Model Context Protocol) servers to execute domino jobs. Domino projects Work with Domino Projects including Git integration, DFS vs Git-based projects, collaboration, and version control. Domino python sdk Programmatically interact with Domino using python-domino SDK and REST APIs. Domino trace setup Set up GenAI tracing for an LLM or agent application in Domino by checking MLflow 3.2.0 and Domino SDK requirements, then adding a tracing_setup.py helper, evaluators, a config.yaml, and @add_tracing / DominoRun wiring. Domino ui bootstrap Bootstrap or retrofit a Vite + React 18 + TypeScript project so it uses the Domino design system (`@dominodatalab/extensions-tools`). Domino ui design Build Domino-styled web applications matching the Domino Design System. Domino workspaces Work with Domino Workspaces - interactive development environments including Jupyter, JupyterLab, VS Code, and RStudio. Netapp volumes Work with Domino Volumes for NetApp ONTAP - enterprise-grade, multi-terabyte storage with near-instant snapshots. Tags and properties Manage Domino tags and properties via the Taxonomy API. 
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