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Amazon Sagemaker was launched by Amazon back in November 2017. It was built with the promise of simplifying machine learning on the cloud. The software was a response not only to the increasing importance of machine learning, but also the fact that there is a demand to perform machine learning in the cloud. Amazon Sagemaker is clearly a smart move by Amazon that will consolidate the dominance of AWS in the cloud market.

What is Amazon Sagemaker?

Amazon Sagemaker is Amazon’s premium cloud-based service which serves as a platform for machine learning developers and data scientists to build, train and deploy machine learning models on the cloud. One of the features that makes Sagemaker stand out from the rest is that it is business-ready. This means machine learning models can be optimized for high performance and deployed at scale to work on data with varying sizes and complexity. The basic intention of Sagemaker, as Vogels mentioned in his keynote, is to remove any barriers that slow down the machine learning process for developers.

In a standard machine learning process, a developer spends most of the time doing the following standard tasks:

  • Collecting, cleaning and preparing the training data set.
  • Selecting the most appropriate algorithm for the machine learning problem
  • Training the model for accurate prediction
  • Optimizing the model’s performance
  • Integrating the model with the application
  • Deploying the application to production

Most of these tasks require a lot of expertise, and more importantly, time and efforts. Not to mention the computational resources such as storage space and processing memory. The larger the dataset, the bigger this problem becomes. Amazon Sagemaker removes these complexities by providing a solid platform with built-in modules that can be used together or individually to complete each of the above tasks with relative ease.

How Amazon Sagemaker Works

Amazon Sagemaker offers a lot of options for machine learning developers to train and optimize their machine learning models to work at scale. For starters, Sagemaker comes integrated with hosted Jupyter notebooks to allow developers to visually explore and analyze their dataset. You can also move your data directly from popular Amazon databases such as RDS, DynamoDB and Redshift into S3 and conduct your analysis there. The simple block diagram below demonstrates the core working of Amazon Sagemaker:

Amazon Sagemaker Architecture

Amazon Sagemaker includes 12 high performance, production-ready algorithms which can be used to build and deploy models at scale. Some of the popular ones include k-means clustering, Principal Component Analysis (PCA), neural topic modeling, and more. It comes pre-configured with popular machine learning and deep learning frameworks such as Tensorflow, PyTorch, Apache MXNet and more, but you can also use your own framework without any hassle.

Once your model is trained, Sagemaker makes use of the AWS’ auto-scaled clusters to deploy the model, making sure the model doesn’t lack in performance and is highly available at all times. Not just that, Sagemaker also includes built-in testing capabilities for you to test and check your model for any issues, before it can be deployed for production.

Benefits of using Amazon Sagemaker

Business are likely to adopt Amazon Sagemaker, mainly because of the fact that it makes the whole machine learning process so effortless. With Sagemaker, it becomes very easy to build and deploy smarter applications that give accurate predictions, and thereby help increase the business profitability.

  • Significantly reduces time: With built-in modules, Sagemaker significantly reduces the time required to do a variety of machine learning tasks, and the models can be deployed to production in very little time. This is important for businesses, as near-real time insights obtained from smart applications help them optimize their processes quickly, and effectively get an edge over their competition.
  • Effortless and more productive machine learning: By virtue of the one-click training and deployment feature offered by Sagemaker, machine learning engineers and developers can now focus on asking the right questions of the data, and focus on the results rather than the process. They can also devote more time to optimizing the model rather than focusing on collecting and cleaning the data, which takes up most of their time.
  • Flexibility in using the algorithms and frameworks: With Sagemaker, developers have the freedom to choose the best-possible algorithm and tool for performing machine learning effectively.
  • Easy integration, access and optimization: The models trained using Sagemaker can be integrated into an existing business application seamlessly, and are optimized for speed and high performance. Backed by the computational power of AWS, business can rest assured their applications will continue to perform optimally without any risk of failure.

Sagemaker – Amazon’s answer to Cloud Auto ML

In a 3-way cloud war between Google, Microsoft and Amazon, it is clear Google and Amazon are trying to go head to head in order to establish their supremacy in the market, especially in the AI space. Sagemaker is Amazon’s answer to Google’s Cloud Auto ML, which was made publicly available in January, and delivers a similar promise – making machine learning easier than ever for developers.

With Amazon serving a large customer-base, a platform like Sagemaker helps them to create a system that runs at scale and handles vast amounts of data quite effortlessly.  Amazon is yet to release any technical paper on how Sagemaker’s streaming algorithms work, but that will certainly be something to look out for in the near future. Considering Amazon identifies AI as key to their future product development, to think of Sagemaker as a better, more complete cloud service which also has deep learning capabilities is definitely not far-fetched.

Data Science Enthusiast. A massive science fiction and Manchester United fan. Loves to read, write and listen to music.


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