What is Amazon Sagemaker?
Amazon SageMaker is a fully managed machine learning service. It enables developers to quickly build, train and deploy machine learning models in the cloud. It provides a built-in Jupyter authoring notebook session for easy access to the data sources for exploration and analysis so one doesn’t have to manage the servers. It also provides common ML algorithms that are optimized to run efficiently against extremely large data-sets in a distributed environment. It has native support for bring-your-own-algorithms and frameworks and thus offers flexible distributed training options that adjust to user’s specific workflows. It helps in deploying a model into a secure and scalable environment by launching it with a single click from the Amazon SageMaker console.
- Studio: An integrated machine learning environment where one can build, train, deploy, and analyze models all in the same application.
- High-quality training datasets.
- SageMaker notebooks that include SSO integration, fast start-up times, and single-click sharing.
- Analyze and pre-process data, tackle feature engineering, and evaluate models.
- Experiment management and tracking.
- Inspect training parameters and data throughout the training process. Automatically detect and alert users to commonly occurring errors such as parameter values getting too large or small.
- Users without ML knowledge can quickly build classification and regression models.
- Monitor and analyze models in production (endpoints) to detect data drift and deviations in model quality.
- Train machine learning models once, then run anywhere in the cloud and at the edge.
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