TL;DR - Amazon SageMaker
- Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models
- It provides notebooks, training infrastructure, and one-click deployment with built-in algorithms
- Pay for compute and storage used, with free tier covering 250 hours of notebook usage
Pricing: Paid only
Best for: Enterprises & professionals
Score: 88/100
Pricing Plans
free trialFree Tier
Free
2 months
- 250 hrs notebooks
- 50 hrs training
- 125 hrs hosting
- ml.t3.medium
Most Popular
On-Demand
$0.23/hour
ml.m5.xlarge training
- All instance types
- Pay for usage
- No commitment
- All features
Savings Plans
Custom
1-3 year terms
- Up to 64% savings
- Flexible instances
- All components
About Amazon SageMaker
SageMaker provides everything needed to build, train, and deploy machine learning models on AWS. Jupyter notebooks for experimentation, managed training infrastructure, one-click deployment to production endpoints.
The platform handles the infrastructure complexity that usually slows ML projects. Automatic model tuning, experiment tracking, and model monitoring keep things manageable as projects scale.
Data science teams use SageMaker to move from experimentation to production without becoming infrastructure experts. It removes the ops burden so you can focus on the models.
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Amazon SageMaker FAQ
AWS Free Tier includes 2 months of SageMaker with limited hours. After that, pay for compute, storage, and features used. Can get expensive quickly for serious ML work.
SageMaker is AWS's machine learning platform. Build, train, and deploy ML models at scale. Includes notebooks, training infrastructure, model hosting, and MLOps tools.
Pay for notebook instances, training jobs, and inference endpoints separately. Prices vary by instance type. Training a model can cost dollars to thousands depending on scale.
Jupyter notebooks for experimentation, managed training on GPU clusters, model hosting with auto-scaling, MLOps pipelines, built-in algorithms, and now SageMaker Studio for collaboration.
Both are comprehensive ML platforms. SageMaker integrates better with AWS services. Vertex AI has AutoML strength. Choose based on your cloud provider preference.