STARK Language — AI Model Deployment

Overview

In STARK, AI model deployment is not an afterthought. It's embedded into the language core, making model serving, scaling, and optimization intuitive and declarative.

Deployment primitives in STARK allow developers to annotate, configure, and deploy their AI models directly through language syntax. These primitives abstract the complexities of model serving infrastructure, enabling smooth scalability and optimal inference performance.

Core AI Deployment Concepts

1. @inference_service Annotation

Marks a model as an inference service with automatic optimization and scaling.

@inference_service(batch_size=32, max_latency_ms=100)
fn sentiment_classifier(text: String) -> Classification:
    let features = tokenize_and_embed(text)
    return model.predict(features)

Behind the scenes:

  • Automatic request batching for throughput
  • GPU memory optimization
  • Load balancing across model replicas
  • Metrics collection and monitoring

2. @model_endpoint Annotation

Exposes a model as a REST/gRPC endpoint with type-safe interfaces.

@model_endpoint(path="/classify", method="POST")
fn classify_text(request: ClassificationRequest) -> ClassificationResponse:
    return sentiment_model.predict(request.text)

Includes:

  • Auto-generated API schema
  • Input validation and error handling
  • Rate limiting and authentication hooks

3. @batch_processor Annotation

Defines a batch processing job for offline inference.

@batch_processor(schedule="0 2 * * *", input_format="jsonl")
fn daily_embeddings_job(data_path: String):
    let dataset = load_dataset(data_path)
    let embeddings = embedding_model.batch_predict(dataset)
    save_embeddings(embeddings, "embeddings.npy")

Execution engine handles:

  • Distributed processing across workers
  • Checkpoint/resume for large jobs
  • Resource allocation and scheduling

4. @model_pipeline Blocks

Define multi-stage AI inference pipelines with automatic optimization.

@model_pipeline
workflow TextAnalysis:
    step tokenize -> embed
    step embed -> classify
    step classify -> postprocess

Features:

  • Automatic pipeline optimization
  • Stage-wise caching and batching
  • GPU memory sharing between stages

5. @model_config

Model-specific configuration with environment-aware deployment.

@model_config
immutable let CLASSIFIER_CONFIG = {
    model_path: ENV("MODEL_PATH"),
    batch_size: 32,
    device: "cuda:0",
    quantization: "int8"
}

Supports:

  • Model versioning and A/B testing
  • Hardware-specific optimizations
  • Runtime configuration updates

6. @auto_scale

GPU-aware auto-scaling for model serving.

@inference_service
@auto_scale(min_replicas=1, max_replicas=10, target_gpu_util=70)
fn image_classifier(image: Tensor<UInt8>[3, 224, 224]) -> Classification:
    return vision_model.predict(image)

Integrates with:

  • GPU cluster management
  • Request queue monitoring
  • Cost optimization engines

AI Deployment Artifact Generation

STARK compiler toolchain (starkc) will generate AI-optimized deployment descriptors:

  • model-service.yaml — Model serving metadata with GPU requirements
  • inference.json — Endpoint specifications with batching config
  • pipeline.starkml — Multi-model workflow definitions
  • model.config — Model-specific configuration and optimization hints

All of these can be bundled into deployable artifacts for one-command deployment:

stark deploy --target=k8s-gpu --env=prod --optimize=latency

AI Model Observability

Each deployment primitive supports AI-specific observability:

  • model_metric(), latency_trace(), and inference_log() functions
  • Automatic A/B testing and model performance tracking
  • Built-in model drift detection and alerting
@inference_service
fn classifier(input: Tensor<Float32>) -> Classification:
    inference_log("request_received", input.shape())
    latency_trace("inference_start")
    let result = model.predict(input)
    model_metric("prediction_confidence", result.confidence)
    return result

Future AI Extensions

  • @edge_inference for CDN-distributed model serving
  • @federated_learning for distributed model training
  • @model_compression for automatic optimization

Summary

STARK makes AI model deployment a first-class construct in language design, not an external ops burden. By bridging ML workflows, performance optimization, and deployment automation, STARK enables ML engineers to deploy models effortlessly at production scale.

Welcome to AI-native deployment—Welcome to STARK.