What is Amazon SageMaker used for?

Amazon SageMaker is an AWS service for developing, training and hosting machine learning models, now positioned as a hub for data, analytics and AI. It is a good fit for training and deploying custom ML models, productionizing data science experiments, and combining analytics and AI work on AWS.

How much does Amazon SageMaker cost?

Amazon SageMaker is a paid product with no free plan.

Is Amazon SageMaker open source?

No. Amazon SageMaker is proprietary (closed-source) software and can't be self-hosted. Open-source alternatives to Amazon SageMaker include MLflow, KubeDL and SGLang.

What are some alternatives to Amazon SageMaker?

Amazon SageMaker competes with Databricks, Dataiku and DataRobot. For open-source options, see Enlisted's ranked list of open-source Amazon SageMaker alternatives.

Amazon SageMaker: features, pricing and alternatives

About Amazon SageMaker

Amazon SageMaker is an AWS platform for machine learning. Data scientists and ML engineers use it to prepare data, build and train models, and deploy them to managed endpoints, with Amazon operating the underlying compute and infrastructure.

AWS now describes the next generation of SageMaker as the center for all of an organization's data, analytics and AI, bringing data work and machine learning into one environment. It builds on long-standing SageMaker capabilities such as notebooks, managed training, model hosting and pipelines, and it connects to AWS storage, analytics and security services.

SageMaker is a hosted, proprietary AWS service, so there is no self-hosted version. It suits data science and ML teams that already work on AWS and want a managed path from experiments to production, and pricing is usage-based as described on its pricing page.

Key features

Managed notebooks for model development
Managed training on scalable compute
Model deployment to hosted endpoints
Pipelines for repeatable ML workflows
Unified environment for data, analytics and AI
Integration with AWS data services

Good fit for

→Training and deploying custom ML models
→Productionizing data science experiments
→Combining analytics and AI work on AWS

Tags: machine-learning; mlops; aws; model-training; model-deployment; ai-infrastructure

Amazon SageMaker: questions and answers

What is Amazon SageMaker used for?: Amazon SageMaker is an AWS service for developing, training and hosting machine learning models, now positioned as a hub for data, analytics and AI. It is a good fit for training and deploying custom ML models, productionizing data science experiments, and combining analytics and AI work on AWS.
How much does Amazon SageMaker cost?: Amazon SageMaker is a paid product with no free plan.
Is Amazon SageMaker open source?: No. Amazon SageMaker is proprietary (closed-source) software and can't be self-hosted. Open-source alternatives to Amazon SageMaker include MLflow, KubeDL and SGLang.
What are some alternatives to Amazon SageMaker?: Amazon SageMaker competes with Databricks, Dataiku and DataRobot. For open-source options, see Enlisted's ranked list of open-source Amazon SageMaker alternatives.