MLA 014 Machine Learning Hosting and Serverless Deployment

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Core Infrastructure

SST uses Pulumi to bridge high-level web components (API, Database) with low-level AWS resources (SageMaker, GPU clusters). The framework enables infrastructure-as-code in TypeScript, allowing developers to manage entire ML lifecycles within a single configuration.

Level 1-2: Foundational Models and Edge Inference

• AWS Bedrock: Managed gateway for models including Claude 4.5, Llama 4, and Nova. It provides IAM security, VPC isolation, and integrated billing.
• Knowledge Bases: Automates RAG pipelines by chunking S3 documents and storing embeddings in Aurora pgvector.
• Cloudflare Workers AI: Runs open-source models (Llama, Mistral, Flux) on edge GPUs. Pricing uses "Neurons" units, measuring compute per request rather than tokens.

Level 3-4: Cost-Effective CPU and Batch Processing

• Lambda Inference: Use ONNX-formatted models on AWS Lambda with SnapStart to minimize costs and 16-second cold starts.
• Vector Search: The SST Vector component manages semantic search within existing Aurora PostgreSQL databases using pgvector, matching dedicated database performance.
• SST Task: Runs Fargate containers for CPU-bound ETL and data preprocessing.
• AWS Batch: Orchestrates GPU training on EC2. Using Spot instances reduces costs by 60 to 90 percent, with checkpointing protecting against instance reclamation.

Level 5: Real-Time GPU Inference

• AWS Options: SageMaker Real-Time endpoints support scale-to-zero since late 2024. SageMaker Async handles large payloads via S3 queues.
• External Alternatives:
  • GCP Cloud Run: Offers serverless L4 and Blackwell GPUs with per-second billing.
  • Modal: Python-native serverless GPU platform with 2 to 4 second cold starts.
  • Groq: Uses LPU hardware for LLM inference, reaching 1300 tokens per second.
  • RunPod: Provides the lowest raw GPU pricing and FlashBoot for fast starts.

Level 6-7: MLOps and Mature Production

• SageMaker Platform: Includes Studio for IDE work, JumpStart for one-click model deployment, and Model Registry for version tracking.
• Monitoring: Use Arize Phoenix or Evidently AI to detect data and concept drift. Log all predictions to S3 for weekly distribution analysis.
• Hardware Optimization: AWS Inferentia and Trainium chips offer 70 percent lower inference costs compared to GPUs. Transition becomes viable when monthly GPU spend exceeds 10,000 dollars.
• Self-Hosting: API calls are cheaper until volume reaches 30 million tokens daily. For self-hosting, use vLLM for high-throughput PagedAttention.