The professional backbone for edge ML. A streamlined pipeline designed to package, quantize, and publish ExecuTorch (.pte) models over the air, ensuring native PyTorch performance and zero-latency local prediction.
The Architecture
Seamlessly convert and quantize PyTorch models to optimized .pte artifacts for native edge inference.
Apply symmetric AES-128-CBC encryption via Fernet to secure proprietary model weights at rest and in transit.
Persist versioned model artifacts to isolated, customer-scoped directories on global CDN or managed storage.
Distribute configuration manifests for instant OTA access, guaranteeing zero-latency local prediction updates.
Seamlessly integrate our lightweight SDK to bridge cloud manifests with live on-device predictions, optimized for real-time performance.
The Contract
Every successful release generates an immutable JSON manifest. This document serves as the absolute contract between the MoM service and your mobile clients, ensuring clients always pull the exact, checksum-verified artifact.
pip install mom-cloud
POST /api/cloud/releases with scoped Bearer tokens.
{
"schema_version": "1.0",
"customer_id": "acme-corp",
"model_id": "vision-classifier-v2",
"version": "1.2.3",
"framework": "pytorch",
"runtime": "executorch",
"artifact_uri": "gs://bucket/releases/acme/...",
"checksum_sha256": "e3b0c44298fc1c149afbf4c...",
"encryption": {
"algorithm": "fernet"
}
}Common Questions
ExecuTorch is PyTorch's official on-device inference runtime, purpose-built for iOS and Android. It compiles PyTorch models to optimized .pte artifacts that run directly on device NPUs and CPUs without a server roundtrip. MoM automates the export and quantization step, so engineering teams get production-ready .pte files from their existing PyTorch checkpoints in a single pipeline stage.
Every model artifact is encrypted using symmetric AES-128-CBC via the Fernet scheme before it leaves the MoM pipeline. Encryption keys are scoped per customer and never stored alongside the artifact. At rest, model files reside in isolated, customer-scoped storage buckets. Your proprietary weights cannot be extracted or tampered with at any point in the delivery chain.
Yes. MoM is designed for regulated industries. All on-device inference means patient or user data never leaves the device — satisfying GDPR's data minimisation principle and HIPAA's minimum necessary standard. The platform is certified GDPR-compliant, HIPAA-compliant, and ISO 27001-accredited. Full compliance documentation is available under our Data Processing Agreement.
MoM currently supports iOS and Android via the ExecuTorch runtime. The lightweight MoM SDK integrates with existing Swift, Kotlin, and React Native projects. Models can target specific hardware backends — Apple Neural Engine (ANE), Qualcomm QNN, or generic CPU — selectable at export time within the pipeline configuration.
After the five-stage pipeline completes, MoM generates an immutable JSON release manifest containing the artifact URI, a SHA-256 checksum, and version metadata. Your mobile clients poll for new manifests and pull updated model files in the background. No app store update is required. The checksum guarantees clients never load a corrupted or tampered artifact, and rollback is a single manifest revert.
Get in Touch
MoM is currently in private beta. Reach out to join the waitlist and get a guided platform walkthrough with our engineering team.