Canso - ML Platform
  • 👋Introduction
  • 🏛️Canso Architecture
  • 💻Getting Started
    • 🏁Overview
    • 🌌Provison K8s Clusters
    • 🚢Install Canso Helm Charts
    • 🐍🔗 Canso Python Client & Web App
    • 📊Health Metrics for Features in the Data Plane
  • 💡Feature Store
    • Data Sources
      • Data Spans
    • Data Sinks
    • ML Features
      • Raw ML Batch Feature
      • Derived ML Batch Feature
      • Raw ML Streaming Feature
      • Custom User Defined Function
  • 💡AI Agents
    • Introduction
    • Getting Started
    • Quickstart
    • Use Cases
      • Fraud Analyst Agent
      • Agent with Memory
      • Memory command examples
    • Concepts
      • Task Server
      • Broker
      • Checkpoint DB
      • Conversation History
      • Memory
    • How Tos
      • Update the AI Agent
      • Delete the AI Agent
    • Toolkit
      • SQL Runner
      • Kubernetes Job
      • Text-to-SQL
    • API Documentation
      • Agent
      • Memory
  • 💡Risk
    • Overview
    • Workflows and Rules
    • Real Time Transaction Monitoring
    • API Documentation
  • 💡Fraud Investigation
    • API Documentation
  • 📝Guides
    • Registry
    • Dry Runs for Batch ML Features
    • Deployment
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On this page
  • Overview
  • Prerequisites
  • How Rule Evaluation Works
  • Monitoring

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  1. 💡Risk

Real Time Transaction Monitoring

PreviousWorkflows and RulesNextAPI Documentation

Last updated 4 months ago

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Overview

The Risk Rule Evaluation Service provides the ability to evaluate transaction in real-time using a REST API. Whenever a workflow is , the Canso platform automatically packages all rules in the workflow and spins up the application. This application runs on the data plane i.e. the customer's Kubernetes cluster. The Canso Developer Agent automates the process of deploying the application and any related infra dependencies.

Once deployed, this application starts accepting requests to evaluate incoming transactions against eligible workflows rules defined as part of the . The application is capable of supporting a double-digit millisecond latency (P99 of <100 ms) in most cases and can autoscale by itself within the limits set at the time of deployment.

The Txn. Monitoring & Evalution app can be configured such that it integrates with Online Feature stores to retrieve feature values.

Prerequisites

To interact with the Risk Evaluation API, ensure you have the following:

  1. Ingress Host and Path:

    • Identify the ingress host and path for your service.

      • Ingress Host: You can find the ingress host by running the following command on your Kubernetes cluster:

        kubectl get ingress -n <namespace> -o yaml

        Look for the HOSTS column to find the appropriate ingress host.

      • Ingress Path: Ensure you also note the PATH associated with your service in the ingress output.

    • Replace {YOUR_INGRESS_HOST} and {YOUR_INGRESS_PATH} in your API calls with the values retrieved from the above command.

  2. Authentication: Basic Authentication is required. Use your Canso credentials:

    • Authorization Header: Authorization: Basic {BASE64_ENCODED_CREDENTIALS}

How Rule Evaluation Works

  1. Rule Retrieval: Fetch active/eligible rules for the specified workflow

  2. Feature Lookups: Retrieve machine learning feature values from the online feature store

  3. Parallel Evaluation: Parallel Evaluation of rules in a workflow for better performance

Monitoring

The service exports the following metrics to Prometheus by default.

  • rule_evaluation_latency_seconds_count

  • rule_evaluation_requests_total

Prometheus is automatically set up in the Data plane i.e. customer's cluster when the Canso Superchart is installed. Refer to for more details.

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Workflow
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