Model Serving

Understands LLM serving basics: inference API setup (vLLM, TGI), prompt/completion endpoint configuration, and token-based billing considerations. Follows team practices for LLM deployment including context window management and response streaming setup.

Deploys ML model as REST API through web framework/Flask. Understands inference pipeline: preprocessing → prediction → postprocessing. Uses pickle/joblib for model serialization.

Understands basic model serving concepts: difference between batch and real-time inference, main model formats (ONNX, SavedModel, pickle). Can deploy a simple model via Flask/FastAPI endpoint, load a model from file, and return predictions. Knows about specialized serving systems — TFServing, Triton, Seldon.

Knows NLP model serving basics: REST API endpoints, model loading, batching. Deploys simple NLP models as REST API for text classification and NER tasks.

Implements LLM serving solutions: KV-cache optimization for throughput, batching strategies (continuous batching, dynamic batching), and quantization for cost-efficient inference (GPTQ, AWQ, GGUF). Configures vLLM/TGI for production workloads. Implements streaming response infrastructure and token-level latency monitoring.

Uses model serving frameworks: Triton, BentoML, Seldon. Configures batch and real-time inference. Optimizes inference latency (ONNX, model optimization). Configures A/B testing for models.

Deploys models to production via specialized serving platforms: TensorFlow Serving for TF models, Triton Inference Server for multi-framework serving. Configures BentoML for packaging models with dependencies, implements batch inference via Spark/Ray, and configures model versioning for seamless production model updates.

Independently designs NLP model serving: TorchServe, Triton Inference Server. Configures batching, model versioning, A/B testing. Optimizes latency through model optimization.

Designs LLM serving architecture: multi-model gateway with intelligent routing, speculative decoding for latency optimization, and disaggregated serving (prefill/decode separation). Implements cost optimization: token budget management, caching layers for repeated prompts, and model cascade strategies. Creates LLM serving benchmarks and capacity planning models. Mentors team on production LLM infrastructure.

Designs model serving architecture. Optimizes throughput (batching, GPU scheduling). Configures autoscaling for ML serving. Implements model fallback and canary deployment.

Architects model serving for complex scenarios: multi-model serving with dynamic loading, ensemble inference via Triton, model A/B testing. Optimizes latency through model optimization (TensorRT, ONNX Runtime), implements GPU sharing for efficient resource utilization, and designs autoscaling based on inference metrics.

Designs high-performance serving infrastructure for NLP models. Optimizes through quantization, distillation, model parallelism. Ensures latency and throughput SLA.

Defines LLM serving strategy for the organization. Establishes inference cost management policies, serving SLA targets, and GPU infrastructure governance. Evaluates serving frameworks (vLLM, TGI, TensorRT-LLM). Conducts architecture reviews for LLM infrastructure. Drives adoption of cost-efficient LLM serving patterns.

Defines model serving strategy for the platform. Designs unified serving layer. Optimizes serving costs. Coordinates with DevOps on infrastructure.

Defines the model serving strategy for the MLOps team: standard stack (KServe/Seldon Core on Kubernetes), deployment patterns (canary, shadow, blue-green). Implements unified model rollout process with mandatory quality checks, configures SLA monitoring for latency, and defines runbooks for inference service incidents.

Defines model serving strategy for the NLP team. Establishes deployment standards, SLA framework, and architectural decisions for scaling NLP inference infrastructure.

Defines organizational LLM serving strategy: inference infrastructure architecture, GPU/TPU procurement strategy, and cost governance for LLM operations. Evaluates build-vs-buy decisions for LLM infrastructure (self-hosted vs API providers). Drives adoption of efficient LLM serving practices and shapes technical vision for AI infrastructure at enterprise scale.

Defines enterprise model serving strategy. Evaluates serving technologies. Designs multi-model serving platform.

Shapes the model serving strategy at the organizational level: unified serving platform for all model types (CV, NLP, tabular), SLA standards. Designs architecture for scaling to thousands of models — model mesh, serverless inference, edge deployment. Defines GPU infrastructure cost optimization strategy for inference and platform roadmap.

Shapes enterprise model serving strategy for the NLP platform. Defines inference infrastructure architecture, optimization standards, and cost management at organizational level.