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Optimized and Secured MLOps for the Edge

Latent AI takes the hard work out of AI optimization with a simplified, repeatable, and transparent edge MLOps workflow that delivers an executable runtime package ready to deploy to edge devices at scale. And it’s model, application, and hardware agnostic.

Latent AI Edge Models

Latent AI lets you take your model, train it, compress it, and then deploy an accurate and secured model designed to work independently on edge devices.

Scalable and Seamless

Latent AI integrates trust into your CI/CD software development flow with model development consistency that means reliable edge scaling and deployment.

Secure

Latent AI builds industry leading security into the model itself with unique identifiers and integrity checks to prevent tampering.

Adaptive

Latent AI Adaptive AI enables edge models to adapt and self-adjust to their workloads depending on their needs and environments, meaning models require less power while maintaining their accuracy.

Edge MLOps

Simplified AI Optimization for an Efficient MLOps Workflow

By applying software development principles to edge models, Latent AI can build model trust with the same continuous cycles of testing and validation. Latent AI gives organizations what they’ve been missing – a repeatable and scalable path for delivering optimized and secured edge models quickly.

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Latent AI Efficient Inference Platform (LEIP)

The Latent AI Efficient Inference Platform™ (LEIP) SDK is an automated edge MLOps pipeline that produces optimized secured edge models at scale. LEIP seamlessly integrates with your current processes to build models already pre-configured for your hardware. Supply it with your model and data, and then receive a deployable runtime with an ultra-efficient model ready and secured for your specific hardware and specifications.

310%

Inference Speed Improvement

<3%

Accuracy Loss (mAP 0.5)

84%

Compression

Example performance vs PyTorch: YOLOv5 Large, AGX, Int8 quantization

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MLOps for the Edge

See how LEIP brings AI to the edge by optimizing models for compute, energy, and memory without requiring changes to existing AI/ML infrastructure and frameworks. It’s a simplified and repeatable MLOps pipeline that produces ultra-efficient, compressed, and secured edge models rapidly and at scale.

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