Image Recognition
Deploy convolutional networks for manufacturing quality inspection at millisecond accuracy.
1. Getting Started
Emra.ls provides a streamlined deployment pipeline for AI/ML models at enterprise scale. We support TensorFlow, PyTorch, and ONNX formats out-of-the-box.
- Containerize your model with built-in Docker templates
- Deploy through our managed AI cluster
- Monitor performance metrics in real-time dashboard
2. Common Use Cases
Natural Language Models
Operationalize BERT/GPT models with scalable APIs for chatbots and document analysis.
Time Series Forecasting
Deploy ARIMA/LSTM models for supply chain demand prediction with daily precision.
Recommendation Systems
Production-ready item collaborative filtering models for retail and content platforms.
3. Deployment Process
Step 1: Containerization
Use the Emra Container Toolkit to package your model with runtime and dependencies.
emra containerize --model my_model.pb
Step 2: Push to Registry
Push your packaged model to cloud storage.
emra push --image my-model:v1
Step 3: Deployment
Deploy your model for production with autoscaling capabilities.
emra deploy my-model:v1 --scale 5
4. Code Example
// Initialize deployment manager
const Deploy = require('@emra/deploy');
// Load model container
const myModel = new Deploy.Model('/mnt/my-model.pb');
// Configure deployment
myModel.configure({
name: "object-detector",
version: "v1.2",
replicas: 3,
autoscale: true
});
// Execute deployment
myModel.deploy()
.then(data => {
console.log('Deployed at:', data.endpoint);
})
.catch(err => {
console.error('Deployment failed:', err);
});
This script packages, pushes, and deploys a pre-trained model to our AI cluster with autoscaling enabled.
5. Post-Deployment Monitoring
Latency
24ms average inference time
Accuracy
98.3% precision score
Usage
85% of quota consumed daily
Need Help?
For more details or technical support, please reach out to our expert team.
Contact Support