Issue link: https://read.uberflip.com/i/1467323
© 2022, Amazon Web Services, Inc. or its Affiliates. In this section – Train, Tune and Deploy PREPARE SageMaker Ground Truth Label training data for machine learning SageMaker Data Wrangler Aggregate and prepare data for machine learning SageMaker Processing Built - in Python, BYO R/Spark SageMaker Feature Store Store, update, retrieve, and share features SageMaker Clarify Detect bias and understand model predictions BUILD SageMaker Studio Notebooks Jupyter notebooks with elastic compute and sharing Built - in and Bring your - own Algorithms Dozens of optimized algorithms or bring your own Local Mode Test and prototype on your local machine SageMaker Autopilot Automatically create machine learning models with full visibility SageMaker JumpStart Pre - built solutions for common use cases TRAIN & TUNE Managed Training Distributed infrastructure management SageMaker Experiments Capture, organize, and compare every step Automatic Model Tuning Hyperparameter optimization Distributed Training Training for large datasets and models SageMaker Debugger Debug and profile training runs Managed Spot Training Reduce training cost by 90% DEPLOY & MANAGE Managed Deployment Fully managed, ultra low latency, high throughput Kubernetes & Kubeflow Integration Simplify Kubernetes - based machine learning Multi - Model Endpoints Reduce cost by hosting multiple models per instance SageMaker Model Monitor Maintain accuracy of deployed models SageMaker Edge Manager Manage and monitor models on edge devices SageMaker Pipelines Workflow orchestration and automation Amazon SageMaker SageMaker Studio Integrated development environment (IDE) for ML