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Amazon SageMaker

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Amazon SageMaker

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.

Traditional ML development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning workflow. You need to stitch together tools and workflows, which is time-consuming and error-prone. SageMaker solves this challenge by providing all of the components used for machine learning in a single toolset so models get to production faster with much less effort and at lower cost.

Features

3-step model

Build

Amazon SageMaker offers you a completely integrated development environment (IDE) for machine learning that lets you improve your productivity. With the help of its one-click Jupyter notebooks, you can build and collaborate with speed. It also offers you a one-click sharing facility for these notebooks. The entire coding structure is captured automatically, which allows you to collaborate with others without any difficulty.

Train

Using Amazon SageMaker Experiments, you can easily organize, track, and evaluate every repetition to machine learning models. Training a machine learning model packs various repetition to measure and isolate the impact of changing algorithm versions, model parameters, and changing datasets. The SageMaker Experiments help you in managing these iterations via capturing the configurations, parameters, and results automatically, and storing them as ‘experiments’.

Deploy

Amazon SageMaker offers you a one-click deployment facility so that you can easily generate predictions for real-time data. You can easily deploy your model on auto-scaling Amazon machine learning instances across various availability zones. You just need to specify the desired maximum and minimum numbers, and the type of instance, and then leave the rest to Amazon SageMaker.

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