Configuration Management

The core paradigm where the desired state of a system (software, firmware, model version, device settings) is defined in code (e.g., YAML, JSON). An automated agent (like an OTA update manager) continuously compares the actual device state to this declared state and performs actions to reconcile any differences. This is superior to imperative scripting as it is idempotent and self-healing.

• Example: A configuration file specifies that all devices in the 'warehouse-sensors' group must run model_v2.tflite and have log_level=INFO. The management system ensures this is true, even if a device reboots or experiences corruption.

Every deployable component—be it a compressed neural network (.tflite), firmware binary, or configuration bundle—is treated as an immutable artifact. Each artifact is assigned a unique version identifier (e.g., semantic version, git hash) and stored in a model registry or binary repository. This enables:

• Reproducibility: Any device state can be precisely recreated.
• Atomic Rollbacks: If a new model causes issues, the system can revert to a known-good previous version instantly.
• Audit Trail: Every deployment is linked to a specific, unchangeable artifact.

Configuration management enforces consistency across development, testing, and production environments to eliminate "it works on my machine" issues. For TinyML, this includes matching compiler toolchains, RTOS versions, and hardware abstraction layer (HAL) settings. Drift detection mechanisms continuously monitor deployed devices for unauthorized changes (e.g., a modified config file) or model drift indicated by performance metrics, automatically triggering alerts or remediation.

• Tool Example: Use of digital signatures on firmware to detect tampering.

The practice of managing and provisioning computing infrastructure—including the device fleet topology, network policies, and update groups—through machine-readable definition files, rather than physical hardware configuration or interactive configuration tools. For microcontroller fleets, this means the logic for canary deployments, rollout waves, and device grouping is defined in version-controlled code.

• Benefit: Enables CI/CD pipelines for embedded systems, where a code merge can automatically trigger build, test, and staged deployment processes.

Secure handling of sensitive data such as Wi-Fi credentials, API keys, digital certificates for device authentication, and model decryption keys. Principles mandate that secrets are never hard-coded into firmware or configuration files. Instead, they are injected at deployment time via secure channels or stored in a Trusted Execution Environment (TEE) on the microcontroller. Access is logged for a complete audit trail.

The coordinated execution of changes across a fleet, following predefined rollout strategies. This includes health checks, automated rollback on failure, and dependency management (e.g., update the RTOS before deploying a new model that requires its features). For TinyML, orchestration must account for offline-first operation, bandwidth constraints, and battery levels, potentially using protocols like MQTT for efficient communication.

• Patterns: Blue-green deployment (switching between two firmware images), shadow mode for model testing, and zero-touch provisioning for new devices.

A declarative management paradigm where a system's target configuration is defined in code (e.g., YAML, JSON). An automated agent (like Ansible, Puppet, or Chef) continuously monitors the system and reconciles its actual state to match this declared desired state. This is central to Infrastructure as Code (IaC) and ensures consistency and compliance across thousands of devices.

• Key Mechanism: Idempotent operations that can be run repeatedly to enforce a defined state.
• TinyML Relevance: Critical for defining the firmware, OS, and model version that should be running on every microcontroller in a fleet.

A method of wirelessly distributing new software, firmware, or machine learning models to a fleet of remote devices. For TinyML, this is the primary mechanism for deploying updated models to microcontrollers in the field without physical access.

• Process: Involves secure download, validation (via digital signatures), and installation of update packages.
• Challenges: Must handle intermittent connectivity, limited bandwidth, and ensure atomic updates to prevent bricking devices.
• Use Case: Pushing a new, more accurate keyword spotting model to a fleet of battery-powered voice remotes.

An automated process for onboarding and configuring new devices into a management system without manual intervention. When a device powers on for the first time, it automatically connects to a provisioning service, authenticates itself, and downloads its initial configuration and software.

• Flow: Device boots → performs device authentication (e.g., with a hardware certificate) → retrieves its unique configuration from a central server.
• Business Value: Enables massive, scalable deployment of IoT sensor networks by eliminating manual setup per device.

A centralized repository for storing, versioning, and managing the lifecycle of machine learning model artifacts. It acts as the single source of truth for models moving from development to production.

• Core Functions: Model versioning, metadata storage (training metrics, data lineage), stage promotion (Staging → Production), and access control.
• TinyML Integration: Stores the quantized .tflite or .onnx model files, along with their associated metadata (expected accuracy, RAM/Flash footprint, target hardware). The configuration management system references a specific model version from the registry for OTA deployment.

A cryptographic mechanism that uses public-key cryptography to verify the authenticity and integrity of software, firmware, or a model file. It proves the artifact was created by a trusted source and has not been tampered with.

• How it Works: The publisher signs the file with a private key, creating a signature. The device verifies the signature using the corresponding public key before installation.
• Critical Role: The cornerstone of secure OTA updates and configuration enforcement, preventing the execution of malicious or corrupted code on edge devices.

A chronological, immutable record of all configuration changes, software deployments, and system events across a device fleet. It provides a verifiable history for compliance, debugging, and forensic analysis.

• Records: Who changed what configuration, when, and why; which model version was deployed to which device group; device authentication and update success/failure logs.
• Importance: Essential for meeting regulatory requirements (e.g., in medical or industrial settings) and for root cause analysis when a deployment causes unexpected behavior.