Model Registry

A model registry provides a single source of truth for all trained model artifacts. This includes the serialized model file (e.g., TensorFlow Lite for Microcontrollers, ONNX), its associated metadata, and versioning information. For TinyML, artifacts are optimized for microcontrollers via quantization and pruning. Storage is immutable, ensuring any deployed model can be precisely reproduced for debugging or compliance.

• Immutable Versioning: Each model upload generates a unique version ID (e.g., model:v1.2.3).
• Metadata Capture: Stores training parameters, dataset version, accuracy metrics, and hardware target (e.g., Cortex-M4).
• Dependency Tracking: Links to the specific ML pipeline run and framework versions used for creation.

This function tracks the complete history of a model, creating an auditable lineage from training to deployment. It answers critical questions: Which dataset was this model trained on? and What changed between version 1.2 and 1.3?

• Provenance Tracking: Automatically logs the data pipeline run, code commit hash, and hyperparameters used for training.
• Comparative Analysis: Allows side-by-side comparison of performance metrics (accuracy, latency, memory footprint) across versions.
• Rollback Capability: Provides a clear path to revert to a previous, stable model version if a new deployment fails, a core requirement for safe canary deployments on edge fleets.

Models progress through predefined stages (e.g., Staging, Production, Archived) that enforce governance and control the promotion path. This gates deployment based on validation criteria.

• Governance Gates: A model cannot be promoted to Production without passing automated tests for accuracy, latency on target hardware, and memory usage.
• Environment Mapping: Stages are linked to specific deployment environments (e.g., Staging maps to a test device fleet, Production maps to the live fleet).
• Audit Compliance: The complete transition history (who promoted what, when, and why) is logged for audit trails, essential for regulated industries.

The registry acts as the control plane for deploying specific model versions to target environments. It integrates with CI/CD pipelines and device management platforms to execute rollout strategies.

• Targeted Rollouts: Supports canary deployments by assigning a new model version to a subset of devices (e.g., 5% of a microcontroller fleet) before full rollout.
• Integration with OTA Systems: Provides the certified model artifact and metadata to Over-the-Air (OTA) update systems for secure delivery to devices.
• Desired State Declaration: Administrators declare the target model version for a device group; the registry and downstream systems reconcile the actual state to match.

Beyond the model file, the registry stores rich metadata that is crucial for evaluation, debugging, and compliance. This turns the registry into a searchable knowledge base for ML artifacts.

• Performance Metrics: Logs accuracy, F1 score, inference latency, and peak RAM/Flash usage for the target microcontroller.
• Artifact Signing: Stores digital signatures to verify model integrity and origin, a key component of secure boot and device authentication chains for TinyML.
• Custom Tags: Allows tagging models with attributes like hardware:esp32 or sensor:imu for efficient filtering and management.

A model registry is not a standalone tool; it's a integrated component within a broader ML pipeline and CI/CD system. It receives models from training pipelines and triggers deployment workflows.

• Pipeline Hook: Automated training pipelines (e.g., using MLflow or Kubeflow) push champion models directly to the registry's Staging stage.
• Trigger for Deployment: Promotion of a model to Production can automatically trigger a pipeline that builds a firmware image, runs hardware-in-the-loop tests, and initiates an OTA update.
• Feedback Loop: Links to model monitoring systems, allowing performance metrics from production devices to be associated with the model version for detecting model drift.

A model registry provides essential version control and lifecycle management for machine learning artifacts. Its primary functions include:

• Artifact Storage: Securely stores model binaries, metadata, and associated files (e.g., training scripts, evaluation reports).
• Versioning: Automatically tracks iterations of a model with unique identifiers (e.g., v1.2.3), enabling lineage tracking and reproducibility.
• Stage Management: Manages model progression through defined lifecycle stages like Staging, Production, and Archived.
• Metadata Cataloging: Attaches critical metadata to each version, including training metrics, data schemas, hyperparameters, and author information.

The registry is a pivotal component in MLOps CI/CD, automating the path from training to deployment. Key integrations include:

• Automated Registration: Training pipelines (e.g., Apache Airflow, GitHub Actions) automatically push validated models to the registry upon successful runs.
• Gated Promotion: Models move from Staging to Production only after passing automated tests for accuracy, latency, and memory footprint—critical for TinyML.
• Deployment Triggers: Promotion to the Production stage can trigger downstream deployment pipelines, such as generating quantized model binaries for specific microcontroller targets or initiating an Over-the-Air (OTA) Update.

The registry acts as the single source of truth for production models, enabling safe deployment strategies essential for managing microcontroller fleets.

• Blue-Green & Canary Deployment: The registry provides the authoritative model binaries for these strategies. It holds both the current (blue) and candidate (green) versions, allowing for instant traffic switching or phased rollouts.
• Instant Rollback: If a new model fails in production (detected via Model Monitoring), operators can instantly revert the registry's Production pointer to a previous, stable version, triggering a rollback deployment.
• A/B Testing: Facilitates serving different model versions to subsets of devices for performance comparison.

Deploying to microcontrollers introduces unique registry requirements beyond standard cloud MLOps.

• Hardware-Aware Artifacts: A single model may have multiple registered variants (e.g., INT8 quantized for MCU A, FP16 for MCU B). Metadata must specify the target hardware and compilation toolchain (e.g., TensorFlow Lite for Microcontrollers, TVM).
• Binary Integrity & Signing: Model binaries pushed to the registry should be cryptographically signed. This digital signature is verified by the device's Secure Boot or Trusted Execution Environment (TEE) before an OTA update is applied.
• Extremely Constrained Validation: Promotion gates must validate not just accuracy but also strict memory usage (<100KB RAM), latency (<10ms), and energy consumption profiles.

An automated sequence of steps that orchestrates the end-to-end machine learning workflow. For TinyML, this pipeline is specialized to handle model compression, quantization, and cross-compilation for target microcontrollers. It feeds directly into the registry by producing the final, deployable model artifact.

• Key Stages: Data preprocessing, model training, compression, validation, and artifact generation.
• TinyML Specifics: Includes steps like converting a TensorFlow model to TensorFlow Lite for Microcontrollers (TFLite Micro).

A centralized data system for storing, serving, and monitoring curated, reusable features for machine learning models. It ensures consistency between the features used during model training and those available during on-device inference. For edge deployments, feature calculation logic must be portable to the microcontroller.

• Core Function: Maintains a single source of truth for feature definitions and transformations.
• Edge Consideration: Requires lightweight, deterministic feature encoders that can run on-device.

The process of deploying a trained model into a production environment where it can receive input and return predictions. In traditional cloud MLOps, this involves a web service API. For TinyML, model serving happens directly on the microcontroller, requiring highly optimized inference engines like TFLite Micro or CMSIS-NN.

• Edge Serving: The model is compiled into the device's firmware. Inference is a local function call, not a network request.
• Constraint: Must operate within strict memory, latency, and power budgets.

A method of wirelessly distributing new software, firmware, or machine learning models to a remote device fleet. The model registry is the source for the new model artifact in an OTA update. For microcontrollers, this requires differential updates and secure signing to minimize bandwidth use and guarantee integrity.

• Registry Integration: The rollout manager fetches a specific model version from the registry and packages it for OTA.
• Critical for TinyML: Enables bug fixes, performance improvements, and drift mitigation across thousands of devices.

The practice of continuously tracking a deployed model's performance, data quality, and operational health. For cloud models, this involves logging prediction inputs and outputs. For TinyML, monitoring is more challenging and often relies on aggregated device telemetry, performance counters, and detecting data drift in sensor input distributions.

• Key Metrics: Inference latency, memory usage, power consumption, and input data statistics.
• Actionable Output: Triggers a retraining pipeline or flags a model version in the registry for rollback.

The systematic handling of changes to a system's settings and parameters using declarative files and version control. In a TinyML context, this manages not just the model artifact, but the entire inference configuration: pre-processing parameters, sensor calibration values, and decision thresholds. The model registry often stores this configuration metadata alongside the model binary.

• Declarative State: The desired firmware configuration, including model version, is defined as code.
• Orchestration: Tools apply this state across the device fleet, ensuring consistency.