Senior Machine Learning Engineer
Production ML engineering patterns for model deployment, MLOps infrastructure, and LLM integration. Covers model serving, feature stores, experiment tracking, A/B testing, and drift monitoring.
🚀 Master production ML engineering with this comprehensive skill. Learn to deploy trained models to production using Docker containers, manage MLOps pipelines with automated training and monitoring, and integrate large language models seamlessly. This skill covers the complete lifecycle from model export and containerization through canary deployments to real-time monitoring and performance validation.
💡 Perfect for teams building scalable AI systems. Use these patterns to set up feature stores, experiment tracking, and model registries. Implement A/B testing infrastructure, drift detection, and automated retraining triggers. Whether you're deploying computer vision models, NLP systems, or complex ML pipelines, these production-ready workflows ensure reliability and performance at scale.
✨ Get battle-tested solutions for real-world challenges: reduce deployment time, catch model degradation before users notice, and maintain high availability across your ML infrastructure.
Requirements
Web framework for building REST APIs
Container platform for model packaging and deployment
Experiment tracking and model registry
Feature store for training data management
High-performance inference server for GPU acceleration