Inside DeepQuantica: Our Foundational Code & Backend for Training ML and LLMs

Why We're Sharing This

Trust in AI engineering starts with transparency. When a company says "we build custom AI models," the obvious question is: how? With what? On what infrastructure? Using what practices?

Most AI companies treat their engineering stack as a trade secret. We think that's backwards. Our competitive advantage isn't in hiding our tools, it's in how well we use them, how deeply we understand our clients' problems, and how reliably we deliver. So here's an honest look at what powers DeepQuantica.

Our Training Infrastructure

Compute Layer

• Primary training: NVIDIA A100 80GB and H100 GPUs via reserved cloud instances
• Development & experimentation: NVIDIA A10G and L4 GPUs for rapid prototyping
• CPU workloads: High-memory instances for data preprocessing, feature engineering, and evaluation pipelines
• Storage: High-throughput NVMe storage for dataset access during training, with S3-compatible object storage for dataset versioning

We use a mix of cloud providers (primarily AWS and GCP) with reserved capacity for predictable costs and spot instances for experimental workloads. Each client project gets dedicated compute allocation, no shared training queues.

Orchestration

• Kubernetes for container orchestration across training and inference workloads
• Custom job scheduler built on top of K8s that manages GPU allocation, priority queuing, and automatic failover
• MLflow for experiment tracking, model versioning, and artifact management
• DVC (Data Version Control) for dataset versioning alongside code

The Training Pipeline

Every model we build goes through a standardized but configurable pipeline:

Stage 1: Data Ingestion & Validation

• Automated data quality checks: schema validation, null detection, distribution analysis
• Data profiling reports generated for every dataset
• PII detection and anonymization where required
• Integration with client data sources via secure connectors (databases, APIs, file systems)

Stage 2: Preprocessing & Feature Engineering

• Tabular data: Automated feature engineering with domain-specific transformations, encoding, and normalization
• Text data: Custom tokenization pipelines, chunking strategies, and cleaning routines optimized for each domain
• Multimodal: Aligned preprocessing for text + image, text + tabular, or any combination

All preprocessing is deterministic and reproducible, same input always produces same output.

Stage 3: Model Training

For traditional ML:

• XGBoost, LightGBM, CatBoost for structured data problems
• Scikit-learn pipelines for classical ML with proper cross-validation
• Optuna for hyperparameter optimization with Bayesian search

For deep learning and LLMs:

• PyTorch as the foundational framework
• Hugging Face Transformers for model architectures and pre-trained weights
• PEFT (Parameter Efficient Fine-Tuning) library for LoRA, QLoRA, and adapter-based training
• TRL (Transformer Reinforcement Learning) for RLHF and DPO training
• DeepSpeed ZeRO for distributed training and memory optimization
• Weights & Biases for experiment tracking and visualization
• Axolotl for streamlined fine-tuning configurations

Stage 4: Evaluation

This is where most teams cut corners. We don't.

• Automated evaluation suites that run on every training checkpoint
• Domain-specific benchmarks co-designed with clients (not just generic metrics)
• Human evaluation protocols for generative tasks with structured rubrics
• Bias and fairness testing across relevant demographic dimensions
• Adversarial testing to find failure modes before production does
• Regression testing to ensure new models don't break existing capabilities

Stage 5: Model Packaging

• Models are exported in standardized formats (ONNX, TorchScript, or native)
• Quantization for inference optimization (GPTQ, AWQ, or dynamic quantization)
• Container images built with pinned dependencies for reproducibility
• Model cards generated with performance metrics, limitations, and intended use cases

Backend Architecture

Inference Serving

• vLLM for high-throughput LLM inference with PagedAttention
• Triton Inference Server for multi-model serving with dynamic batching
• TGI (Text Generation Inference) for Hugging Face model deployment
• FastAPI microservices for custom business logic wrapping model inference
• Redis for caching frequent queries and reducing redundant inference

API Layer

• RESTful APIs with comprehensive OpenAPI documentation
• gRPC for low-latency internal communication between services
• WebSocket support for streaming inference results
• Rate limiting and authentication via API gateway (Kong or custom)
• Request validation and sanitization at every entry point

Data Layer

• PostgreSQL for structured data, metadata, and application state
• Redis for caching, session management, and real-time features
• Elasticsearch for log aggregation and semantic search
• S3-compatible storage for model artifacts, datasets, and backups
• Vector databases (Pinecone, Qdrant, or pgvector) for RAG implementations

Monitoring & Observability

• Prometheus + Grafana for infrastructure and model performance metrics
• Custom drift detection pipelines monitoring input/output distributions
• Alerting via PagerDuty with escalation policies
• Structured logging with correlation IDs for end-to-end request tracing
• Cost tracking per model, per client, per endpoint

Code Quality & Practices

Version Control

• Git with trunk-based development
• Mandatory code reviews for all changes
• Automated CI/CD with testing gates (unit, integration, model performance)
• Infrastructure as Code (Terraform) for all cloud resources

Testing

• Unit tests for all data transformations and business logic
• Integration tests for the full training pipeline
• End-to-end tests for inference APIs
• Load testing before every production deployment
• Shadow deployments for new model versions

Security

• SOC2-aligned practices for data handling
• Encryption at rest and in transit
• Role-based access control with audit logging
• Regular dependency scanning and vulnerability assessments
• Client data isolation at the infrastructure level

Why This Matters

When you hire DeepQuantica, you're not getting a team that duct-tapes APIs together and calls it AI. You're getting:

• Battle-tested infrastructure that's been refined across dozens of deployments
• Reproducible pipelines where every model can be retrained identically
• Production-grade serving that handles real traffic with real SLAs
• Comprehensive testing that catches issues before your users do
• Full transparency into how your system works and why

We don't believe in black boxes. Your AI system should be something you understand, trust, and can maintain. That's what our engineering foundation enables.

Conclusion

The tools we use are not secret. Most of them are open source. Our value is in the engineering discipline, domain expertise, and production experience that turns these tools into reliable, business-critical systems.

If you want a deeper dive into any specific part of our stack, or want to discuss how it applies to your use case, reach out. We're always happy to talk engineering.