Deployment

MIND targets cloud, edge, and on-premise deployment. Serving infrastructure, auto-scaling integration, and monitoring ship with the commercial mind-runtime.

Deployment Options

MIND supports multiple deployment targets:

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:

Pure-MIND Protocol & Crypto Primitives

The networking layer is building toward a fully self-hosted stack. MIND now includes a library of crypto and protocol primitives written in pure MIND and verified against RFC and NIST known-answer tests: AES-128-GCM, SHA-256, HKDF, X25519, X.509 parsing and signature verification, SHA-3/SHAKE (Keccak), RSA-PSS, ECDSA-P256, ML-KEM-768 (FIPS 203 post-quantum), the TLS 1.3 key schedule, record layer, and handshake transcript (replay-verified against RFC 8448), HPACK (RFC 7541), and HTTP/2 framing (RFC 9113).

Honest scope: this is a verified primitive library, not yet a working TLS client or server — there is no socket-driven handshake state machine or certificate-chain path validation yet, and HPACK is decode-only. The primitives are correctness-first, not speed-optimized.

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.