Developers can deploy their own external analytics in delair.ai platform using the Command Line Interface. The integration of custom models enables to use the platform ressources and features for visual and non-visual data management, adding external analytics to end-to-end workflows.
This tutorial shows how to configure an external analytic description and deploy the analytic.
1. Analytic Components
The analytic is mainly defined by :
- A name
- An algorithm
- Inputs
- Parameters
- Outputs
- Computation requirements
2. Analytic Description File
The analytic description file is a single YAML file. Here is an example :
---
spec-version: v0.1
name: eiffel-corp/rust-detector
display-name: Rust detector
description: Detects rust on historical buildings
docker-image: 889324968982.dkr.ecr.us-east-1.amazonaws.com/rust-detector:v1.0
instance-type: small
volume-size: 50
groups: [Maintenance]
inputs:
- name: building_mesh
display-name: Building 3D Model
description: 3D Model of the model (mesh dataset)
kind: dataset
schema:
type: mesh
required: true
parameters:
- name: detection-mode
display-name: Rust detection mode
description: Level of detection (Fast, Standard or Deep)
schema:
type: string
enum:
- Fast
- Standard
- Deep
required: true
deliverables:
- name: rust-report
display-name: Rust detection report
description: Pdf report containing insights about rust detected in the 3D model
kind: dataset
schema:
type: file
required: true
3. Model Deployment
Deployment is achieved in three steps :
Step 1 - Package your model in a Docker image
Step 2 - Push it to your Docker registry
Step 3 - Create the analytic with the Command Line Interface
4. Analytic Availability in Platform
Step 1 - Go to the Analytics section from the left bar to open the catalog :
Step 2 - Open the newly created section (in our example below called BETA) and select your newly integrated analytic (Geo Scatter Plot in example) :
Step 3 - Run your analytic :
Learn here about delair.ai SDK.