Amazon SageMaker

Amazon SageMaker


Amazon SageMaker helps data scientists and inventors to prepare, make, train, and deploy high-quality machine learning models by bringing together a broad set of capabilities purpose- erected for machine learning.

Amazon SageMaker makes available a set of solutions for the most common use cases that may be deployed readily with just a few clicks to make it easier to grow started.


Amazon SageMaker is a completely accomplished machine learning service. Data scientists and developers may speedily and easily build and train machine learning models with SageMaker. They can straight deploy them into a production-ready hosted environment.

Amazon SageMaker offers an integrated Jupyter authoring notebook example for easy access to the data sources for assessment and analysis. Therefore, we don’t have to manage servers. It similarly makes available common machine learning algorithms that are enhanced to run well against extremely large data in a distributed environment.

Amazon SageMaker deals with flexible distributed training options that amend to the particular workflows with built-in support for bring-your-own-algorithms and frameworks. Deploy a model into a safe and scalable environment by beginning it with a few clicks from SageMaker Studio.

Amazon SageMaker

Main Features of Amazon SageMaker

Amazon SageMaker comprises the following features:

SageMaker Studio

A combined machine learning environment where we can build, train, deploy, and analyze the models all in the same application.

SageMaker Canvas

This is an auto-machine learning service. It provides people with no coding experience the ability to shape models and make predictions with them.

SageMaker Ground Truth Plus

This is a turnkey data labeling feature. It is used to create high-quality training datasets without having to construct labeling applications. It manages the labeling workforce on its own.

SageMaker Studio Lab

It is a free service that provides customers access to AWS workout resources in an environment based on open-source JupyterLab.

SageMaker Training Compiler

It trains deep learning models faster on scalable GPU examples managed by SageMaker.

SageMaker Studio Universal Notebook

Effortlessly discover, link to, create, dismiss and manage Amazon EMR clusters in a single account. Also available in cross account configurations directly from SageMaker Studio.

SageMaker Serverless Endpoints

This is available as a serverless endpoint option for hosting the machine learning model. Automatically scales inability to serve the endpoint traffic. Eliminates the requirement to select instance types or manage scaling policies on an endpoint.

SageMaker Inference Recommender

Develop recommendations on inference instance types and configurations. For example, example count, container parameters, and model optimizations to use the machine learning models and workloads.

SageMaker Model Registry

This is cross-account support for deployment of the machine learning models such as versioning, artifact and heredity tracking, endorsement, and workflow.

SageMaker Projects

Make end-to-end Machine Learning solutions with CI/CD with the help of SageMaker projects.

SageMaker Model Building Pipelines

Build and manage machine learning pipelines combined directly with SageMaker jobs.

SageMaker Data Wrangler

We may add Data Wrangler into the machine learning workflows to make it simpler and streamline data pre-processing and feature engineering using little to no coding. We can also enlarge our own Python scripts and changes to modify the data prep workflow.

SageMaker Feature Store

This is a consolidated store for features and related metadata. Therefore, features may be simply discovered and reused. We can make both Online and Offline stores. The Online Store may be used for low potential, real-time inference use cases. The Offline Store may be used for training and batch inference.

SageMaker JumpStart

We can learn about SageMaker features and competencies by curated one-click solutions, example notebooks, and pre-trained models that we can deploy. We may also fine-tune the models and deploy them.

SageMaker Clarify

Develop the machine learning models by identifying possible biases and help explain the predictions that models make.

SageMaker Edge Manager

Improve custom models for edge devices, make and manage fleets and run models with a well-organized runtime.

SageMaker Ground Truth

High-quality training datasets by using workers accompanied by machine learning to create labeled datasets.

Amazon Augmented AI

Build the workflows essential for human review of Machine Learning predictions. Amazon A2I takes along the human review to all developers. It removes the undistinguishable heavy lifting related to building human review systems.

SageMaker Experiments

We can use the tracked data to rebuild an experiment and incrementally build on experiments led by peers.

SageMaker Debugger

Examine training parameters and data in the training process. Automatically sense and alert users to usually happening errors for example parameter values getting as well large or small.

SageMaker Autopilot

Users deprived of machine learning knowledge may rapidly build classification and regression models.

SageMaker Model Monitor

Monitor and examine models in production to sense data drift and deviations in model quality.

SageMaker Neo

We can train machine learning models on one occasion, then run anywhere in the cloud and at the edge.

SageMaker Elastic Inference

Accelerate the throughput and reduce the latency of receiving real-time inferences.

Reinforcement Learning

Make the most of the long-term reward that an agent obtains as a result of its actions.


Examine and pre-process data, tackle feature engineering, and assess models.

Batch Transform

Pre-process datasets, and run inference when we don’t require a determined endpoint. Also, link input records with inferences to help the interpretation of outcomes.

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