Backend & APIs · open-edge-platform/edge-ai-libraries

Build a new time-series analytics use case on top of the deployed Time Series Analytics microservice — bring it up with Docker Compose (from a repo clone, or by fetching the compose files from…

open-edge-platform/edge-ai-libraries

Scaffolds and wires a new NestJS service/module for the Video Search & Summarization sample app's pipeline-manager using the repo's real conventions.

open-edge-platform/edge-ai-libraries

Build (and optionally push) the VSS Docker images from source with make build, make build-deps, and make push - the application services, the dependency microservices, or both, with registry/tag…

open-edge-platform/edge-ai-libraries

Deploy and operate DL Streamer Pipeline Server — a microservice that wraps DL Streamer pipelines behind a REST API for containerized, no-code operation.

open-edge-platform/edge-ai-libraries

Extend, test, debug, or integrate the Model Download microservice codebase.

open-edge-platform/edge-ai-libraries

Download and convert AI models using the Model Download microservice.

open-edge-platform/edge-ai-libraries

Develop and debug the Multimodal DataPrep microservice: its FastAPI media endpoints, in-process embedding pipeline, batch jobs, object detection, telemetry and Metrics Manager publishing, and…

open-edge-platform/edge-ai-libraries

Develop the Multimodal Embedding Serving microservice itself — Poetry install, run the existing tests, navigate the wrapper/registry/handler architecture, add a new model family, and build the image…

open-edge-platform/edge-ai-libraries

Develop the Time Series Analytics microservice itself (FastAPI + Kapacitor) — build and deploy it locally via Docker Compose or Helm, run the mocked unit test suite (tests/runtests.sh) and the…

open-edge-platform/edge-ai-libraries

Deploy and consume Intel Multimodal DataPrep from prebuilt images or a repository checkout.

open-edge-platform/edge-ai-libraries