ML Deployment

Understands the fundamentals of ML Deployment. Applies basic practices in daily work. Follows recommendations from the team and documentation.

Knows ML model deployment basics: REST API, model serialization, inference server. Deploys simple ML models via web framework with basic error handling under mentor guidance.

Independently deploys ML models to production using MLflow, BentoML, or TorchServe. Configures model versioning, A/B testing, and canary releases. Monitors prediction quality and data drift through automated alerts.

Independently deploys ML models to production: TorchServe, BentoML, model optimization (ONNX, TensorRT). Configures prediction monitoring, data drift detection, and A/B testing.

Designs ML serving infrastructure for high-throughput production systems. Optimizes inference through model optimization: ONNX, TensorRT, quantization. Implements feature store integration, online/offline serving, shadow mode for new models.

Designs ML serving infrastructure for the LLM ecosystem: unified serving layer for ML and LLM models, feature stores, online/offline prediction pipelines with low latency requirements.

Defines ML deployment strategy for the data science team. Establishes production readiness standards: SLA, monitoring, rollback procedures. Coordinates ML engineering and DevOps for building a reliable ML serving platform.

Defines ML deployment standards for the LLM team. Establishes guidelines for model serving, versioning, rollback strategy. Coordinates infrastructure for different ML model types.

Shapes ML infrastructure strategy at organizational level. Defines investments in ML serving platforms, evaluates managed vs self-hosted solutions. Designs scalable architecture for hundreds of models in production.

Shapes enterprise ML serving platform. Defines approaches to unified model serving for ML and LLM, cost optimization, and capacity planning. Ensures SLA for critical prediction services.