Amazon SageMaker Reviews

Name: Amazon SageMaker
Brand: Amazon Web Services (AWS)
Rating: 4 (27 reviews)

Vendor: Amazon Web Services (AWS)

3.8 out of 5

27 reviews
88% willing to recommend

1,262 followers

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What is Amazon SageMaker?

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

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Amazon SageMaker is the #4 ranked solution in top AI Development Platforms and #5 ranked solution in top Data Science Platforms. PeerSpot users give Amazon SageMaker an average rating of 7.6 out of 10. Based on the analysis of the 27 most recent Amazon SageMaker reviews, the overall sentiment is positive, with a sentiment score of 9.1. (The highest in the category). Amazon SageMaker is most commonly compared to Databricks: Amazon SageMaker vs Databricks. Amazon SageMaker is popular among the large enterprise segment, accounting for 66% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 18% of all views.

Helped 814,763 peers since 2012

Featured reviews

Natu Lauchande

Tech Lead - Sanlam Fintech Cluster - Data,ML,AI Eng. at Sanlam

We use the product for deploying machine learning models. We use it for the machine learning model development process We're currently implementing a project on a cross-selling model. It is like a standard XGBoost model. I’m evaluating the tool to see whether it will improve the workflow.…

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AnupKumar3

Executive Specialists at LineData

We use the solution to extract financial information and contractual data from unstructured documents The product provides the ability to develop AI models relatively quickly. My team develops the models using the tool. We use AI quite extensively in our business. We use the tool for predictive…

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Ethan Kang

Senior Actuary at Accelerant Holdings

The primary use case for Amazon SageMaker involves developing on the cloud to utilize flexible compute resources. I use it when dealing with really large datasets that do not fit on my local laptop. I leverage SageMaker's more powerful GPU, CPU, and memory for these tasks Amazon SageMaker, in…

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Amazon SageMaker mindshare

Product category:

As of November 2024, the mindshare of Amazon SageMaker in the Data Science Platforms category stands at 7.7%, down from 10.4% compared to the previous year, according to calculations based on PeerSpot user engagement data.

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Key learnings from peers

Last updated Nov 1, 2024

Valuable Features

"SageMaker offers functionalities like Jupyter Notebooks for development, built-in algorithms, model tuning, and options to deploy models on managed infrastructure."
"It's user-friendly for business teams as they can understand many aspects through the AWS interface."
"The intuitive interface and streamlined user experience make it easy to navigate and set up various tools like Visual Studio Code or Jupyter Notebook."

Room for Improvement

"The user interface (UI) and user experience (UX) of SageMaker and AWS, in general, need improvement as they are not intuitive and require substantial time to learn how to use specific services."
"Amazon might need to emphasize its capabilities in generative models more effectively."
"When starting a new session, the waiting time can be quite long, ranging from two to five minutes."

Pricing

"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."
"In terms of pricing, I'd also rate it ten out of ten because it's been beneficial compared to other solutions."
"The tool's pricing is reasonable."