Why Amazon SageMaker Is The Ultimate Choice (2021)

Amazon SageMaker Studio – First IDE for Machine Learning – Amazon Web  Services
Credit: aws amazon sagemaker studio

Machine learning (ML) is one of the most rapidly evolving fields of technology and a highly sought-after skill set in today’s job market. According to the World Economic Forum, the growth of artificial intelligence (AI) could generate 58 million net new jobs in the coming years, despite the fact that there are currently 300,000 AI engineers worldwide, with millions more needed.

We need to develop and train machine learning models quickly, and then deploy them directly into a hosted environment; Amazon SageMaker will assist us in this endeavour. Amazon SageMaker is a service that allows developers to create and train machine learning models for predictive or analytical applications on the Amazon Web Services (AWS) public cloud. With this service, you can easily create machine learning models and prepare them for training by using all of the resources it provides to quickly attach to your training data and pick and optimise the best algorithms for the application.

Exploring Amazon SageMaker's new features -- CloudFormation, Data Wrangler  | VentureBeat


Amazon SageMaker makes it simple to create ML models and train them by providing everything you need to quickly link to your training data and pick and optimise the right algorithm and system for your application. Amazon SageMaker provides hosted Jupyter notebooks for exploring and visualising your Amazon S3-stored training results. You can link to data in S3 directly, or use AWS Glue to transfer data from Amazon RDS, Amazon DynamoDB, and Amazon Redshift into S3 for review in your notebook.

Amazon SageMaker contains the ten most popular machine learning algorithms, which have been pre-installed and optimised to offer up to ten times the efficiency you’ll find running these algorithms anywhere else. Amazon SageMaker is also pre-configured to run the common open-source frameworks TensorFlow and Apache MXNet. You can also build your own framework.

Start Your Machine Learning on AWS SageMaker | by Guang X | Servian | Medium


With a single click on the Amazon SageMaker console, you can begin training your model. Amazon SageMaker handles all of the underlying infrastructures and can quickly scale to train models on a petabyte-scale. Amazon SageMaker will automatically tune the model to achieve the highest possible accuracy, making the training process even quicker and easier.


Once your model has been trained and tuned, Amazon SageMaker makes it simple to deploy it in production and begin making predictions on new data (a process called inference). Amazon SageMaker deploys the model on an auto-scaling cluster of Amazon EC2 instances spread across several availability zones to provide both high performance and high availability. Amazon SageMaker also offers built-in A/B testing capabilities to assist you in testing the model and playing with various models to obtain the best performance.

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Companies using Amazon SageMaker:

Amazon SageMaker is used by 832 businesses, according to our results. Amazon SageMaker is most widely used by businesses in the United States and the Computer Software industry. Amazon SageMaker is most widely used for corporations with more than ten thousand employees and more than one billion dollars in sales. Our Amazon SageMaker utilisation data goes back one year and three months.

Who uses Amazon SageMaker?

Amazon SageMaker is used by the following businesses:

CompanyWebsiteCountryRevenueCompany Size
Halliburton Cohalliburton.comUnited States>1000M>10000
3M Co3m.comUnited States>1000M>10000

SageMaker is not a free programme. You are paid by the second on Amazon. It will be free if we use it within the first two months of signing up with Amazon.

Why did you purchase this product or service?

  • Reduce time to market
  • Drive revenue growth
  • Improve compliance & risk management
  • Enhance decision making
  • Improve customer relations/service
  • Improve business process agility
  • Create internal/operational efficiencies
  • Cost management

What were the key factors that drove your decision?

  • Pre-existing relationships
  • Strong customer focus
  • Product functionality and performance
  • Strong services expertise
  • Strong consulting partnership
  • Strong user community
  • Overall cost
  • Breadth of services

Top Alternatives to Amazon SageMaker:-

  1. IBM Watson Studio – IBM Watson Studio speeds up the computer and deep learning workflows needed to integrate AI into your company and drive innovation. It provides a set of tools that enable data scientists, application developers, and subject matter experts to work collaboratively and easily with data to create, train, and deploy models at scale.

2. Google Cloud AI Platform -Google Cloud AI Platform is a service that allows users to easily construct machine learning models that can work with any type of data, of any scale.

3. Azure Machine Learning Studio – Azure Machine Learning Studio is a graphical user interface (GUI)-based integrated development environment for creating and deploying Machine Learning workflows on Azure.

4. TensorFlow – TensorFlow is an open-source software library that uses data flow graphs to perform numerical computations.

5. DataRobot -DataRobot provides a machine learning framework that allows data scientists of all ability levels to develop and deploy accurate predictive models in a fraction of the time it used to take.

6. IBM Watson Machine Learning – Create, train, and deploy machine learning and deep learning models using your own data. Using an integrated, collaborative workflow to quickly and confidently create intelligent business applications.

7. Dataiku DSS – Dataiku is the product development platform for data professionals. This all-in-one tool combines all of the capabilities needed to create end-to-end highly specialised services that rapidly transform raw data into business impacting predictions.

8. MATLAB – MathWorks’ MATLAB is a programming, modelling, and simulation platform.


For most data scientists who want to achieve a completely end-to-end ML solution, AWS SageMaker has been a great deal. It abstracts a large number of software development skills required to complete the task while remaining highly effective, scalable, and cost-effective. Most significantly, it allows you to concentrate on the core ML experiments while supplementing the remaining skills with simple abstracted resources similar to our current workflow.

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