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Backed By Amazon Web Services, Inc.
Use this AI to build, train, and deploy ML models for data scientists & developers on AWS.
Amazon SageMaker is a fully managed service from AWS that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly. It supports automated data labeling, built-in algorithms, distributed training, and one-click deployment. SageMaker integrates with the AWS ecosystem, supports Jupyter notebooks, and provides MLOps tools for monitoring and governance.
Automated data labeling
Built-in and custom algorithms
Distributed training
One-click deployment
Model monitoring and governance
Integrate with AWS services
250 hours/month of ml.t3.medium notebook instances
No upfront commitment
Commitment to consistent usage (1 or 3 years)
SageMaker Studio (Unified IDE)
SageMaker Canvas (No-code ML)
SageMaker Autopilot (AutoML)
SageMaker JumpStart (Pre-trained models)
SageMaker Pipelines (CI/CD for ML)
Real user experiences from across different platforms
Amazon SageMaker Model Registry transformed our ML deployment workflow... dramatically accelerating our path to production.
David Preble
2024-05-15
Enterprises with existing AWS infrastructure
L lifecycle
Absolute beginners to cloud computing
s
Democratizes machine learning by providing a fully managed, end-to-end service that scales automatically, reducing the complexity and cost of maintaining ML infrastructure.
Steep learning curve for beginners Cost complexity can lead to unexpected bills if not monitored Vendor lock-in to the AWS ecosystem