ML Engineer Toolkit

End-to-end ML pipeline templates with feature engineering, training, evaluation, and deployment stages.

Deploy models with FastAPI, TensorFlow Serving, Triton, and BentoML. Includes A/B testing and canary patterns.

Build a feature store with Feast or custom implementation. Feature engineering, versioning, and serving patterns.

MLflow and Weights & Biases configurations with experiment comparison, model registry, and artifact management.

Annotation workflow tools, quality assurance scripts, active learning selectors, and labeling interface templates.

Drift detection, performance monitoring, and alerting for deployed models with Grafana dashboards and alerting rules.

Text processing pipelines, embedding workflows, fine-tuning scripts, and RAG implementation patterns.

Image classification, object detection, and segmentation pipelines with data augmentation and evaluation frameworks.

Build LLM-powered apps with prompt management, RAG pipelines, guardrails, evaluation harnesses, and cost tracking.

Test ML code: data validation, model performance, fairness checks, integration tests, and regression detection.

CUDA memory management, mixed precision training, distributed training configs, and GPU utilization monitoring.