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<\/p>\nWith Amazon SageMaker, Machine Learning team can build customer-facing services. This product is a blend of HTTP API’s, low and high-level SDK’s, and an AWS Console UI. Data labeling is normally the most time-consuming part of the process of building ML models based on custom data. Amazon SageMaker Ground Truth helps in this case, by contribution of easy access to public and private human labelers. Moreover, Ground Truth will kick-off a private active-learning model; learning from human labels to make high-quality, automatic annotations that significantly lower labeling costs by up to 70%.<\/p>\n
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There are many elements provided by SageMaker as:
\n\u2022 Notebook instances: full managed jupyter-notebook instance where machine learning code can be used with access to all other AWS services.
\n\u2022 Training jobs: the element to accomplish model training job
\n\u2022 Models: the element to accomplish trained models
\n\u2022 Endpoints: full accomplished web service that can handle requests (HTTP or others) as input and make forecasts as responses<\/p>\n
SageMaker makes widespread use of Docker containers to let users to train and deploy algorithms. Containers allow data scientists to package software into standardized units that run constantly on any platform that supports Docker. When you build a model in Amazon SageMaker, you can deliver distinct Docker images for the training code and the inference code.<\/p>\n
ML developers need a work environment to build ML models. Jupyter Notebooks are very normally used for that. With a few clicks, any practitioner can get managed EC2 machines with Jupyter Notebooks up and running, pre-configured with the most popular data science tools like Scikit-learn, Pandas, TensorFlow, MXNet and PyTorch. With the Notebook instances, SageMaker facilitates integration with Git repositories, allowing unified collaboration and sharing among ML developers.<\/p>\n
SageMaker provides a deep-learning model compiler, \u201cSageMaker Neo.\u201d Neo lets data scientists train models once and deploy them anywhere, in the cloud or at the edge, with up to 2X performance improvement at 1\/10th the size of the original framework. This is very valuable for low-power, low-memory devices commonly used for IOT. Neo can compile and optimize machine learning models with no loss in accuracy, for target hardware platforms from Intel, Nvidia, Arm, Cadence, Qualcomm, and Xilinx.<\/p>\n