Overview
Product Information on Amazon SageMaker AI
Updated 7th May 2026
What is Amazon SageMaker AI?
Amazon SageMaker AI Pricing
Overall experience with Amazon SageMaker AI
Data And Analytics Manager
30B + USD, Insurance (except health)FAVORABLE
“AWS Sagemaker Streamlines Iteration and Collaboration in MLOps Amid Cost Challenges”
Using AWS Sagemaker has been a gamechanger for my organization's data science workflows delivering seamless end to end automation prioritizing streamlined collaboration and rapid iteration. Huge focus on governance and training makes it a robust choice for scalable MLOps.
SENIOR SYSTEMS ENGINEER
500M - 1B USD, BankingCRITICAL
“Sagemaker Enables Efficient Data Handling Yet Complexity and Platform Dependency Noted”
We have a lot of data and are trying to prototype ML models to train them on our datasets. We hope that the models we train will help us to boost efficiency of our processes and improve our infrastructure.
About Company
Company Description
Amazon Web Services (AWS), established in 2006, is focused on providing essential infrastructure services to businesses globally in the form of cloud computing. The key advantage offered through cloud computing, particularly via AWS, is its capacity to shift fixed infrastructure expenses into flexible costs. Businesses have been able to forgo extensive planning and procurement of servers and other Information Technology (IT) resources, owing to AWS. AWS seeks to provide businesses with prompt and cost-effective access to resources using Amazon's expertise and economies of scale, as and when their business requires. Currently, AWS offers a robust, scalable, economic infrastructure platform on the cloud powering an extensive array of businesses worldwide. It operates across numerous industries with data center locations in various parts of the globe including U.S., Europe, Singapore, and Japan.
Company Details
Company type
Public
Year Founded
2006
Head office location
Seattle, United States
Number of employees
10001+
Website
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- Data And Analytics Manager10B+ USDInsurance (except health)Review Source
AWS Sagemaker Streamlines Iteration and Collaboration in MLOps Amid Cost Challenges
Using AWS Sagemaker has been a gamechanger for my organization's data science workflows delivering seamless end to end automation prioritizing streamlined collaboration and rapid iteration. Huge focus on governance and training makes it a robust choice for scalable MLOps. - Manager, It Security And Risk Management<50M USDIT ServicesReview Source
Integration with other tools facilitates the control and adjustment of processes
I highly value this service individually because we've been able to cover the entire machine learning service, creating, training, and deploying models without touching our physical infrastructure. However, learning how it works has brought us more than a few headaches... - Director of Product<50M USDServices (non-Government)Review Source
Sagemaker Model Package Simplifies Deployment but Lacks End-User Guidance
Creating a new model package is very easy. We use Sagemaker studio for experiments with different models. Sagemaker model package is the easiest and safest way to deploy our models to customers. - SENIOR SYSTEMS ENGINEER50M-1B USDBankingReview Source
Sagemaker Enables Efficient Data Handling Yet Complexity and Platform Dependency Noted
We have a lot of data and are trying to prototype ML models to train them on our datasets. We hope that the models we train will help us to boost efficiency of our processes and improve our infrastructure. - Research and Development Manager10B+ USDManufacturingReview Source
Easy use of large-scale computing resources and the difficulty of endpoint design
I think this fits the needs of those who want to apply machine learning on a large scale but don't want to pay machine costs all the time, and above all, it's easy to get started.
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