ADVANCED GENERATIVE ARTIFICIAL
INTELLIGENCE
Monitoring & Governance
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Automated Alerts: Quality degradation, latency spikes, cost anomalies, bias drift
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Model Cards: Automatic documentation of model capabilities, limitations, ethical considerations
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Lineage Tracking: Full traceability from data sources through training to deployment
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Compliance Auditing: Automated checks for regulatory requirements, bias metrics, privacy preservation
CORE ARCHITECTURAL COMPONENTS
Training & Fine-Tuning Automation
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Automated Training: Triggered by data updates or model improvements
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Hyperparameter Tuning: Optuna, Ray Tune for automated optimization
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Distributed Training: Automatic cluster provisioning and teardown
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Cost Optimization: Spot instance usage, automatic checkpointing, early stopping
Model Deployment Pipeline
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Shadow Deployments: Test new models against production traffic without serving responses
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Canary Releases: Gradual rollout (1% → 10% → 50% → 100%) with automated rollback
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A/B Testing: Multi-armed bandit for model selection, statistical significance testing
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Blue-Green Deployments: Zero-downtime model updates with instant rollback capability
Model Development Pipeline
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Experiment Tracking: Automatic logging of hyperparameters, metrics, artifacts (MLflow, W&B)
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Version Control: Git-based model code, DVC for datasets, model registry for weights
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Automated Testing: Unit tests for data processing, integration tests for model APIs, regression tests for quality metrics
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Code Quality: Pre-commit hooks, linting (ruff, black), type checking (mypy)
Technology Stack
Core Languages: PyTorch, JAX, TensorFlow, vLLM, Megatron-LM
Model Training & Fine-Tuning: Accelerate, PEFT, TRL, Axolotl, LitGPT
Inference Optimization: vLLM, llama.cpp, ONNX Runtime, Triton Inference Service, Text Generation Inference, TensorRT-LLM
Vector Databases & Search: Chroma, FAISS, PineCone, Weaviate, ElasticSearch, Milvus
RAG & Document Processing: LangChain, LlamaIndex, Haystak, Unstructured.io, Docling, Marker
Agent Frameworks: AutoGen, CrewAI, LangGraph, Semantic Kernel, OpenAI Assistant
Evaluation & Testing: RAGAS, DeepEval, PromptTools, MLFlow, Weights/Biases, Arize
Orchestration & Deployment: Kubernetes, Docker, Terraform, k3S, Helm, MLFlow, BentoML, Seldon Core
Data & Processing: Apache Spark, Dask, Delta Lake, DVC
Monitoring & Observability: Grafan, Langfuse, Helicone, Phoenix Arize, Datadog APM
GPU & Compute Infrastructure: NVIDIA GPUs, AMD GPUs, Google TPUs, CUDA/cuDNN, Triton
Cloud Platforms & MLOps: AWS, GCP, Azure, Databricks, Modal, Anyscale, SnowFlake
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