Version control system. A service that stores model source code for development and deployment procedures (e.g. a GitHub Repository).
Trained Model Binary
An archive containing a trained ML/AI model (inference code, model weights, etc). Odahu defines a format for these binaries. See <ref_model_format.html>
Application that uses model source code, Data Bindings, Connections and Training Hyperparameters to produce a Trained Model Binary.
Data Binding
Reference to remote data (e.g. files from S3) should be placed for a Train process.
Credentials for an external system. For example: Docker Registry, cloud storage location, etc.
Training Hyperparameters
Parameter for Training process. For example, count of epochs in evolution algorithms.
A containerized process that converts model source code, Data Bindings, Connections and Training Hyperparameters to Trained Model Binary using a Trainer defined in a Trainer Extension
Trainer Extension
A pluggable Train implementation.
Containerized application that uses a Trained Model Binary and Connections and converts them into a target Archive. Typically this is a Docker image with REST API.
Containerized process which turns a Trained Model Binary into a Docker image with REST API using a Packager Extension.
Packager Extension
A pluggable Package implementation.
Containerized application that uses the results of a Package process and Connections to deploy a packaged model on a Kubernetes cluster.
Containerized process that deploys results of a Package operation to Kubernetes cluster with a REST web service.
Trainer Metrics
Metrics set by Trainer code during Train (e.g. accuracy of model). These metrics can be used for querying and comparing Train events.
Trainer Tags
Key/value value pairs that are set by Trainer code (e.g. type of algorithm). Can be used for querying and comparing Train runs.
General Python Prediction Interface
Format of storing models, written in a Python language
MLflow Trainer
Integration of MLflow library for training models, written in a Python. Details - MLFlow Trainer
REST API Packager
Integration for packing trained models into Docker Image with served via REST API
API service
API for managing Odahu Platform resources for cloud deployed Platform
A Kubernetes Operator that manages Kubernetes resources (Pods, Services and etc.) for Odahu Train, Package, and Deploy instances.
A deployed model output, given input parameters.
Model prediction API
API provided by deployed models to allow users to request predictions through a web service.
Prediction Feedback
Feedback versus the previous prediction, e.g. prediction correctness.
Model Feedback API
An API for gathering Prediction Feedback
Feedback aggregator
A service that provides a Model Feedback API and gathers input and output prediction requests
Odahu-flow SDK
An extensible Python client library for API service, written in Python language. Can be installed from PyPi.
Odahu-flow CLI
Command Line Interface for API service, written in Python. Can be installed from PyPi. It uses the Odahu-flow SDK.
Plugin for JupyterLab
A odahu-specific plugin that provides Odahu Platform management controls in JupyterLab.
Plugin for Jenkins
A library for managing Odahu Platform resources from Jenkins Pipelines.
Plugin for Airflow
A library that provides Hooks and Operators for managing Odahu Platform resources from Airflow.
Model Deployment Access Role Name
Name of scope or role for accessing model deployments.
JWT Token
A JSON Web Token that allows users to query deployed models and to provide feedback. This token contains an encoded role name.
A/B testing
Process of splitting predictions between multiple Model Deployments in order to compare prediction metrics and Model Feedback for models, which can vary by source code, dataset and/or training hyperparameters
Odahu distribution
A collection of Docker Images, Python packages, or NPM packages, which are publicly available for installation as a composable Odahu Platform.
Odahu Helm Chart
A YAML definition for Helm that defines a Odahu Platform deployed on a Kubernetes cluster.
Odahu-flow’s CRDs

Objects that API service creates for actions that require computing resources to be stored. For example: connections, Trains, etc.

These objects are Kubernetes Custom Resources and are managed by operator.

Identity Provider (idP)
A component that provides information about an entity (user or service).
Policy Enforcement Point (PEP)
A component that enforces security policies against each request to API or other protected resources.
Policy Decision Point (PDP)
A component that decides whether the request (action in the system) should be permitted or not.