What is AWS SageMaker?
AWS SageMaker is a managed service provided by Amazon Web Services (AWS) that allows data scientists and developers to build, train, and deploy machine learning models quickly and efficiently. It provides a fully managed platform with integrated tools for the entire machine learning workflow.
Key Features of AWS SageMaker
- One-Click feature for Training the models. Amazon SageMaker trains the model by first specifying the location of data, indicating the type of SageMaker instances, and getting started with a single click. It sets up the distributed compute cluster, performs training, outputs results to the Amazon S3, and further tears down the cluster.
- Amazon SageMaker provides Distributed training. Amazon SageMaker makes it faster for its users to perform the distributed training by splitting the data across multiple GPUs, achieving near-linear scaling efficiency. It also helps separate the model across multiple GPUs by automatically profiling and partitioning it with fewer than 10 lines of code.
- Amazon SageMaker helps in Profiling and Debugging Training Runs. Amazon SageMaker Debugger captures the metrics and profiles training jobs in real-time enabling users to correct performance problems quickly before the model is deployed to production.
- Amazon SageMaker supports Reinforcement Learning. Amazon SageMaker supports reinforcement learning and traditional supervised and unsupervised learning. It has built-in, fully-managed reinforcement learning algorithms including the newest and best performing in academic literature.
- Amazon SageMaker supports AutoML. Amazon SageMaker autopilot automatically builds, trains, and tunes the best machine learning models, based on the users' data while allowing them to maintain complete control and visibility. The model can be deployed to production with just one click or iterated to improve the model quality further.
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Learn How AWS SageMaker Compares With Others
Both Saturn Cloud and AWS SageMaker are leading platforms that offer powerful tools to streamline your workflow, but cater to different needs and preferences.
Regarding cost, ease of use, scalability and performance, integration, and more, see how they compare here.
