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What is Amazon SageMaker?

[fa icon="calendar"] 22/02/18 09:05 by Editorial Team

Editorial Team


Amazon SageMaker is an entirely-managed service that helps data scientists and developers to swiftly and simply build, train and implement machine learning models of any magnitude. It eliminates any obstacle that might slow down developers seeking to make use of ML.

Machine learning without Amazon Sagemaker

Often, machine learning tends to appear more complicated than it should to many developers since the process to train and build models before hosting them is rather slow and hard. The initial step involves collection and preparation of training data to determine the data elements you should use. From, there, you choose the algorithm and framework to use.

Once you have settled on your preferred approach, you are required to teach the model how to come up with predictions through training. This process requires a lot of computing for it to work. Proceed to tune your model to ensure it always gives the best predictions; this can turn out to be very tedious since it requires manual effort.

After coming up with a fully trained model, integrate it with your application and then implement the application on scalable infrastructure. All this requires expertise, availability of huge amounts of compute and storage. You will also need a lot of time to test and optimise each part of the process. It is due to all this work involved that the whole thing appears infeasible to most developers.

Amazon SageMaker tries to do away with the complexity that keeps developers from succeeding with every step. It comes with modules that can either be used together or on their own to help in building, training and hosting the ML modules.


How does Amazon SageMaker work?


With this service, you can easily build machine learning models and get them ready to be trained through all the tools it offers to swiftly get connected to your training data and to choose and optimise the ideal algorithms for the application.

The service has hosted Jupyter notebooks that help you to explore and visualise the training data kept in Amazon S3. It is possible to directly connect information in S3 or utilise the AWS Glue to shift data from Amazon Redshift and DynamoDB for processing in your store.

This service has a dozen of the most used ML algorithms that come preinstalled to assist you in selecting your preferred algorithm. These are usually optimised so that they can deliver 10 times the performance you would achieve if you ran the algorithms anywhere else.

This service is also pre-configured so that it can run Apache MXNet and TensorFlow, which are well-known open source frameworks. Alternatively, you can use your own framework.



Through a single click in the service console, it is easy to start training your model. Amazon SageMaker takes care of every infrastructure on your behalf and can easily scale to train the models at petabyte scale.

In order to speed and simplify the training process, it can automatically tune the model to get the best accuracy.



After your model has been successfully trained and tuned, it is easy to implement it in production so that you can begin producing predictions on fresh data. Your model gets hosted on an auto-scaling cluster of Amazon EC2 instances spread throughout many availability zones for high performance and availability.

This service does away with the heavy lifting of ML to help you build, train and implement/host ML models swiftly and with ease. AWS KMS-based Encryption is also available in this service to help in training and deploying.


Categories: AWS

Editorial Team

Written by Editorial Team

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