AWS SageMaker ML

AWS SageMaker is the managed ML service for the entire ML lifecycle: data labeling, feature engineering, training, tuning, deployment, monitoring. Use pre-built algorithms (XGBoost, linear learner, image classification) or bring your own via containers. Key skills: notebook instances for exploration, training job orchestration, hyperparameter tuning, endpoint deployment, model monitoring, cost optimization. SageMaker abstracts away Kubernetes, distributed training complexity, and infrastructure management. Why it matters: reduces time-to-model from months to weeks, scales training on massive datasets, handles A/B testing natively. Salary: $180k–$250k for senior ML engineers at companies using SageMaker (Airbnb, Snap, Stripe). Learning path: 2 weeks basics (notebook + training job), 2 weeks intermediate (hyperparameter tuning, deployment), 2 months production (monitoring, retraining, cost optimization).