Deployment | MIND

MIND provides production-ready deployment options for cloud, edge, and on-premise environments with built-in serving infrastructure, auto-scaling, and monitoring.

Early Access: Deployment features are currently in Phase 15 development. The APIs described here are subject to change. See the Roadmap for current status.

Deployment Options

MIND supports multiple deployment targets:

Target	Use Case	Features
Container	Kubernetes, Docker	Auto-scaling, health checks, rolling updates
Serverless	AWS Lambda, Cloud Run	Pay-per-request, automatic scaling
Edge	IoT, mobile, embedded	Optimized binary, low latency
Bare Metal	On-premise, HPC	Maximum performance, custom hardware

Quick Start

Deploy a trained model with a single command:

# Build optimized inference binary
mind build --release model.mind -o model.bin

# Deploy to local serving endpoint
mind serve model.bin --port 8080

# Or deploy to cloud
mind deploy model.bin --target aws --region us-east-1

Container Deployment

Generate production-ready Docker images with the MIND CLI:

# Generate Dockerfile and build image
mind container build --image my-model:v1.0 model.mind

# Push to registry
docker push my-model:v1.0

# Deploy to Kubernetes
mind deploy model.mind \
    --target kubernetes \
    --replicas 3 \
    --cpu 2 \
    --memory 4Gi \
    --gpu 1

Kubernetes Manifest

MIND generates Kubernetes manifests with best practices:

# Generated by: mind deploy --target kubernetes
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-model
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-model
  template:
    spec:
      containers:
      - name: inference
        image: my-model:v1.0
        ports:
        - containerPort: 8080
        resources:
          requests:
            cpu: "2"
            memory: "4Gi"
            nvidia.com/gpu: "1"
        readinessProbe:
          httpGet:
            path: /health
            port: 8080
        livenessProbe:
          httpGet:
            path: /health
            port: 8080

Model Serving

MIND's built-in serving infrastructure provides REST and gRPC endpoints:

use mind::serve::{Server, Endpoint};

// Define inference endpoint
@endpoint("/predict")
fn predict(input: Tensor<f32, [1, 784]>) -> Tensor<f32, [1, 10]> {
    model.forward(input)
}

// Start server with auto-batching
fn main() {
    Server::new()
        .model(model)
        .batch_size(32)
        .batch_timeout_ms(10)
        .port(8080)
        .serve();
}

API Endpoints

The serving runtime exposes these endpoints:

Endpoint	Method	Description
/predict	POST	Run inference on input data
/health	GET	Health check for load balancers
/metrics	GET	Prometheus metrics
/info	GET	Model metadata and version

Edge Deployment

Deploy optimized models to edge devices with minimal runtime:

# Build for ARM64 edge device
mind build --target aarch64-unknown-linux-gnu \
    --optimize size \
    --quantize int8 \
    model.mind -o model-edge.bin

# Build for WebAssembly (browser)
mind build --target wasm32 \
    --optimize size \
    model.mind -o model.wasm

Optimization Options

Quantization: INT8/INT4 inference for reduced memory and faster execution
Pruning: Remove redundant weights for smaller models
Fusion: Combine operators for fewer kernel launches
Static shapes: Compile with fixed input shapes for optimized code paths

A/B Testing & Canary Deployments

MIND supports gradual rollouts and traffic splitting:

# Deploy new version with 10% traffic
mind deploy model-v2.mind \
    --canary \
    --traffic-percent 10 \
    --monitor latency,accuracy

# Promote to 100% after validation
mind deploy promote model-v2

# Rollback if issues detected
mind deploy rollback

Monitoring & Observability

Built-in monitoring with OpenTelemetry integration:

use mind::serve::{Server, Metrics};

Server::new()
    .model(model)
    .metrics(Metrics::new()
        .histogram("latency_ms")
        .counter("requests_total")
        .gauge("batch_size")
        .enable_tracing())
    .otlp_endpoint("http://collector:4317")
    .serve();

Available Metrics

Inference latency: p50, p95, p99 latency histograms
Throughput: Requests per second, batch utilization
Resource usage: GPU memory, CPU utilization
Model drift: Input/output distribution changes
Error rates: Failed predictions, timeouts

Model Versioning

Track and manage model versions with built-in versioning:

# Tag a model version
mind model tag model.mind v1.2.0 --message "Improved accuracy"

# List versions
mind model versions

# Deploy specific version
mind deploy model@v1.2.0

# Compare versions
mind model diff v1.1.0 v1.2.0

Best Practices

Always use versioned model artifacts in production
Enable health checks and readiness probes for container deployments
Use auto-batching to maximize throughput under load
Set resource limits to prevent OOM issues
Monitor model drift and retrain when accuracy degrades
Use canary deployments for risky changes

Learn More

See Distributed Execution for multi-node deployment and the Roadmap for upcoming deployment features.