Ways to build
| Concept | What it is |
|---|---|
| Workspaces | A self-serve interactive development environment using the IDE of your choice, designed to feel like a “laptop in the cloud.” Intended for exploratory analysis and interactive development. |
| Jobs | A batch run of a script or command, fully reproducible, for training, simulation, and data processing. Can be run manually, via APIs, or on a schedule. |
| Experiments | Useful primarily when developing machine learning models, Experiments provide a way to track, organize, and compare different model configurations you’re testing. |
| Flows | Multi-step computation graphs. A good fit for data engineering tasks or MLOps tasks. |
| Compute clusters | Frameworks for running highly parallelizable workloads, including Spark, Ray, Dask, and Slurm. Domino can attach clusters to Workspaces, Jobs, and other workloads and manages the necessary infrastructure, including autoscaling. |
Use data
| Concept | What it is |
|---|---|
| Datasets | Versioned filesystem storage for large data, shared across Projects, with strong features for reproducibility. |
| NetApp Volumes | Like Datasets but backed by NetApp ONTAP storage, with near-instant snapshots regardless of size. Use them wherever your organization runs ONTAP. |
| Data Sources | Connectors to external databases, data warehouses, data lakes, and object stores such as Amazon S3. |
| External Data Volumes | Network-attached storage, such as network file system (NFS) shares or Windows shares, mounted into the workloads you run. |
Productize what you build
| Concept | What it is |
|---|---|
| Domino endpoints | An HTTP API wrapped around your code, meant for real-time inference tasks such as classification and scoring. |
| Apps | Interactive web applications built with frameworks such as Streamlit, Dash, Shiny, Flask, or any other web framework. Domino handles routing, authentication, and scaling. |
| Agents | Programs that use a large language model (LLM) to plan and act across multiple steps. Domino hosts and serves them alongside Apps and traces them for evaluation and monitoring. |
| Launchers | Self-serve web forms that run a predefined Job with the parameters a less technical user fills in. Useful for turning scripts into simple web interfaces. |
Govern
| Concept | What it is |
|---|---|
| LLM Gateway | A router that lets you direct LLM calls to any underlying model, with usage tracking, cost controls, and guardrails. Contact your Domino field team for access. |
| Governance policies | Policies define the stages, required evidence, and required approvals that a work product, such as an App or model, must go through as it’s built. Used to enforce standards for internal workflows and compliance policies. |
| FinOps | Manage and monitor infrastructure spend. |
| Audit Trail | A record of user activity for compliance verification, security investigations, and audit reporting. |
Collaborate, reproduce, and manage knowledge
| Concept | What it is |
|---|---|
| Projects | A shared space for people to collaborate with access to the same set of code, data, results, and history. The primary way of organizing work in Domino. |
| Reproducibility | Domino keeps records of all the “inputs” for most deliverables and results you create in the platform: the code, data, and Compute Environment that produced a given result or Model or App. |
| Search | Domino indexes contents in the platform so you can find and reuse past work easily. |
| Project templates | A Project pre-configured with code, data, and other materials, to be used as a starting point for new Projects to help people use best practices and avoid re-inventing the wheel. |
| Tags and Properties | Tags are controlled, hierarchical labels for categorizing and finding assets. Properties are typed custom fields that enrich them. |
Architecture and administration
| Concept | What it is |
|---|---|
| Compute orchestration | Domino runs every workload as a container on Kubernetes. A cluster autoscaler adds compute capacity as demand grows. |
| Hybrid and multicloud compute | One Domino deployment can run workloads in several regions, clouds, or on-premises data centers, each a Data Plane attached to the same Control Plane. |
| Compute Environments | A versioned, shareable definition of the tools and packages a workload runs in, built from a Docker image, so everyone runs the same software. |
| Hardware Tiers | Administrator-defined compute profiles (CPU, memory, GPU) that you pick when launching a workload. Access can be limited to an organization. |