SageMaker Model Monitor to maintain high-quality models.SageMaker Debugger to debug anomalies during training. SageMaker Experiments to organize and track your training jobs and versions.SageMaker Autopilot to automatically create ML models with full visibility.SageMaker Pipelines to automate and manage ML workflows.You can use many services from SageMaker Studio, AWS SDK for Python (Boto3), or AWS CLI, including: Using SageMaker Studio, you pay only for the underlying compute and storage that you use within Studio. SageMaker Studio gives you complete access and visibility into each step required to build, train, and deploy models. You can now access Amazon SageMaker Studio, the first fully integrated development environment (IDE) at no additional charge. Free Tier usage per month for the first 2 monthsĢ50 hours of ml.t3.medium instance on Studio notebooks OR 250 hours of ml.t2 medium instance or ml.t3.medium instance on notebook instancesĢ50 hours of ml.t3.medium instance on RSession app AND free ml.t3.medium instance for RStudioServerPro appġ0 million write units, 10 million read units, 25 GB storageĥ0 hours of m4.xlarge or m5.xlarge instancesġ25 hours of m4.xlarge or m5.xlarge instancesġ50,000 seconds of on-demand inference durationġ60 hours/month for session time, and up to 10 model creation requests/month, each with up to 1 million cells/model creation requestįree Tier usage per month for the first 6 monthsġ00,000 metric records ingested per month, 1 million metric records retrieved per month, and 100,000 metric records stored per month
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