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Amazon SageMaker

Amazon SageMaker enables developers and data scientists to easily build ML models.

Amazon SageMaker is a fully managed service that allows developers and data scientists to build, train, and deploy machine learning (ML) models much faster and efficiently for your specific use cases. Relying on a single toolset, SageMaker makes the steps of the machine learning process more seamless, resulting in developing higher quality models, faster time to production, and significant cost savings.

Collection and Preparation of Training Data:

  • Easy data source connecting for the preparation of data and creation of model features
  • Provides developers with deeper visibility into training data and models
  • Comprehensive security features that support a wide range of industry regulations
  • Rapid data labeling with custom or built-in workflows
  • Scalability and reliability for data processing workloads

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Training and Tuning Models:

  • Track, store, manage, browse, and compare iterations to ML models called “experiments”
  • Debugging and profiling features enable simplified performance problem correcting
  • Managed Spot Training can reduce cost associated with training jobs by up to 90%
  • Automatic Model Tuning allows for easy algorithm parameter adjustment for the most accurate predictions, and significant time savings
  • One-click model training
  • Distributed training features can split data across multiple GPUs with automatic profiling and partitioning of models

Deploying Models to Production:

  • Fully automated CI/CD workflows for the entirety of the ML lifecycle
  • Automatic and continuous model monitoring with alerting
  • Built-in human review workflows
  • Batch Transform uses a simple API and allows users to run predictions on larger and smaller batch datasets without resizing
  • Kubernetes Integration
  • One-click production deployment
  • Deploy large numbers of machine learning models with Multi-Model endpoints in a scalable and cost-effective manner