Intermediate

Mist Cloud Architecture

Understand the cloud-native microservices architecture that powers Mist AI, including the data lake, ML pipeline, and how the platform scales to support millions of network devices.

Microservices Architecture

The Mist Cloud is built on a microservices architecture where each service handles a specific domain. This design enables independent scaling, rapid feature deployment, and fault isolation.

Service LayerFunctionKey Technologies
Data IngestionCollects telemetry from all managed devicesStreaming protocols, message queues
Data LakeStores raw and processed telemetry at scaleTime-series databases, object storage
ML PipelineTrains, validates, and deploys AI modelsTensorFlow, custom ML frameworks
API GatewayExposes REST APIs and webhook integrationsOpenAPI, event-driven architecture
DashboardPresents insights and management interfaceReal-time visualization, SLE dashboards

Data Collection and Processing

  1. Device Telemetry Streaming

    APs, switches, and gateways continuously stream granular telemetry data to the cloud, including per-client metrics, RF environment scans, and protocol-level events.

  2. Event Correlation

    Raw events are correlated across devices and time windows to build a comprehensive picture of network state and user experience.

  3. Feature Engineering

    Raw data is transformed into ML-ready features: time-series aggregations, statistical distributions, and behavioral patterns.

  4. Model Training

    ML models are trained on aggregated data from all customers (with privacy controls), benefiting from the collective intelligence of the entire Mist customer base.

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Architecture Tip: The Mist Cloud's multi-tenant architecture means your network benefits from ML models trained on data from thousands of deployments, while your data remains isolated and secure.

Cloud Advantages

Zero Infrastructure

No on-premises controllers or management servers to maintain. Reduce operational overhead and eliminate single points of failure.

Continuous Updates

New AI features, model improvements, and bug fixes are deployed continuously without customer downtime or maintenance windows.

Global Scale

Manage networks across hundreds of sites worldwide from a single cloud instance with consistent AI-driven insights everywhere.

API-First Design

Every dashboard action has a corresponding API, enabling full automation and integration with existing IT service management tools.

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Looking Ahead: In the next lesson, we will explore Marvis Virtual Network Assistant, the conversational AI that makes Mist Cloud intelligence accessible through natural language.