Synthetic Data Generation Toolkit

Production AI Infrastructure: Synthetic Data Generation Toolkit

After deploying retrieval-augmented generation systems that processed 2.3 million patient records across three hospital networks and building multi-agent orchestration pipelines for defense intelligence classification, I packaged every hard-won configuration, prompt template, and evaluation harness into this toolkit. The Synthetic Data Generation Toolkit addresses the exact failure modes I encountered scaling AI from proof-of-concept to production on AWS SageMaker, Azure ML, and GCP Vertex AI.

Most AI toolkits hand you a Jupyter notebook and call it done. This one ships what production teams actually need: infrastructure-as-code for GPU cluster provisioning (A100/H100 configurations with spot instance fallback), model serving manifests for both real-time and batch inference endpoints, and a complete evaluation framework that measures hallucination rates, latency percentiles (p50/p95/p99), and cost-per-inference across providers.

What Is Included

• Terraform modules for SageMaker endpoints, Vertex AI pipelines, and Azure ML managed compute with auto-scaling policies tuned for GPU workloads (scale-to-zero during off-peak, burst to 8 nodes under load)
• RAG pipeline templates with ChromaDB, Pinecone, and pgvector configurations — includes chunking strategies (semantic vs. fixed-size vs. recursive) benchmarked against retrieval accuracy on domain-specific corpora
• Multi-agent orchestration framework using LangGraph and CrewAI patterns with circuit breakers, retry logic, and token budget management that prevented $47K in runaway API costs during my defense contract work
• Model evaluation harness covering BLEU, ROUGE-L, BERTScore, and custom faithfulness metrics with automated regression detection across model versions
• Prompt engineering library — 60+ production-tested prompt templates for summarization, classification, extraction, and code generation with version control and A/B testing hooks
• MLOps pipeline definitions for GitHub Actions and GitLab CI: model training, evaluation, registry push, canary deployment, and automated rollback on metric degradation
• Cost optimization playbook with spot instance strategies, model distillation workflows (GPT-4 to fine-tuned Llama 3), and inference caching patterns that reduced our per-query cost from $0.034 to $0.003

Compute and Integration Requirements

Minimum viable setup: 1x NVIDIA T4 (16GB VRAM) for inference, 4x A10G for fine-tuning. Production recommendation: A100 40GB or H100 80GB instances. All templates include CPU-only fallback configurations for development and testing. Integration points cover OpenAI API, Anthropic Claude, AWS Bedrock, Azure OpenAI Service, and self-hosted vLLM/TGI endpoints behind a unified abstraction layer.

The evaluation framework plugs into Weights & Biases, MLflow, or standalone HTML dashboards. Every component includes docker-compose.yml for local development and Helm charts for Kubernetes deployment. GPU memory profiling scripts identify OOM risks before they hit production.

Who This Is Built For

ML engineers moving from notebooks to production, platform teams building internal AI infrastructure, and architects designing multi-model systems. If you have spent a weekend debugging CUDA driver mismatches or watched a fine-tuning job fail at hour 11 because your checkpointing configuration was wrong, this toolkit eliminates those failure modes permanently. Every configuration has been validated against real workloads processing millions of documents, not toy datasets.