Amazon SageMaker Reviews

Name: Amazon SageMaker
Brand: Amazon Web Services (AWS)
Rating: 3.9 (33 reviews)

Vendor: Amazon Web Services (AWS)

3.9 out of 5

33 reviews
90% willing to recommend

1,273 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 #3 ranked solution in top Data Science Platforms and #4 ranked solution in top AI Development Platforms. PeerSpot users give Amazon SageMaker an average rating of 7.8 out of 10. Based on the analysis of the 33 most recent Amazon SageMaker reviews, the overall sentiment is positive, with a sentiment score of 7.1. 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 824,052 peers since 2012

Featured reviews

Natu Lauchande

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

SageMaker Studio sounds very interesting. Feature Store and data pipeline features are very interesting. The product is a one-stop shop. It allows people without much engineering knowledge to try out and deploy models in environments similar to the production environments. The tool makes our ML model development a bit more efficient because everything is in one environment. It is easy to manage compared to when things were in different components of AWS. Amazon SageMaker is in AWS, so I need not pay two bills. It is one less system to manage, so it is easier.

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AnupKumar3

Executive Specialists at LineData

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 analytics. It helps predict which trade might fail based on historical data. Automatic Model Tuning helps improve the productivity of the investment operation team. Typically, an analyst spends about 45% of their time collecting, organizing, and ingesting data. We were able to use the product to automate processes.

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Ethan Kang

Senior Actuary at Accelerant Holdings

There are two areas in SageMaker that need improvement. Firstly, when starting a new session, the waiting time can be quite long, ranging from two to five minutes. Secondly, the integration with Snowflake is not optimal. I face challenges in authentication, requiring multiple steps to achieve single sign-on. This process feels somewhat cumbersome.

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Amazon SageMaker mindshare

Product category:

As of December 2024, the mindshare of Amazon SageMaker in the Data Science Platforms category stands at 7.5%, down from 10.1% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Data Science Platforms

PeerAnalyst reports

Type	Title	Date
Category	Data Science Platforms	Dec 25, 2024	Download
Product	Reviews, tips, and advice from real users	Dec 25, 2024	Download
Comparison	Amazon SageMaker vs Databricks	Dec 25, 2024	Download
Comparison	Amazon SageMaker vs KNIME	Dec 25, 2024	Download
Comparison	Amazon SageMaker vs Microsoft Azure Machine Learning Studio	Dec 25, 2024	Download

Title	Rating	Mindshare	Recommending
Databricks	4.1	19.2%	96%	84 interviews Add to research
Microsoft Azure Machine Learning Studio	3.8	5.9%	93%	58 interviews Add to research

Valuable Features

Amazon SageMaker's most valuable features include Random Cut Forest, integration with AWS services, IDE, deployment ease, model control, Canvas, Autopilot, and hyperparameter tuning. Users benefit from seamless integration with Python, accelerated hardware, algorithm-based modeling, AutoML, feature store, streamlined machine learning pipelines, flexibility, intuitive interfaces, and comprehensive functionalities. It simplifies data preparation, model deployment, monitoring, and efficient scalability, making it user-friendly for businesses and data scientists.

"I recommend SageMaker for ML projects if you need to build models from scratch."
"SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project."
"I appreciate the ease of use in Amazon SageMaker."

Room for Improvement

Amazon SageMaker requires enhancements in its interface and documentation. Pricing complexity and high costs are major concerns. Integration with platforms like Hadoop and Apache Spark should be streamlined, and security measures should be bolstered. The platform's user experience needs improvement to facilitate ease of use for beginners. Feature adaptability, particularly for large datasets, and better support for various data protocols are needed. Training resources, encompassing online modules and practical use cases, would benefit new users.

"The entry point can be a bit difficult. Having all documentation easily accessible on the front page of SageMaker would be a great improvement."
"Having all documentation easily accessible on the front page of SageMaker would be a great improvement."
"I would recommend having more walkthrough videos and articles beyond AWS Skill Builder."

ROI

Companies experienced ROI by using Amazon SageMaker, notably saving time and reducing costs in machine learning processes. Users found its pre-configured components efficient, benefiting businesses running on AWS. While initial deployment was advantageous, concerns arose about long-term expenses. Developing applications with SageMaker contributed to significant financial savings, such as one organization saving approximately three million dollars. The tool's quick AI model initiation attracted interest, but ongoing cost considerations influenced its regular usage.

Pricing

Amazon SageMaker pricing is complex and varies based on machine type, storage, and computation needs. Users can estimate costs using a provided tool. Generally, costs are tied to compute resources, with monthly prices ranging from $7,000 to $15,000. Additional costs may include enhanced support services. SageMaker is seen as expensive, sometimes rated six to eight out of ten. Despite some seeing it as cost-efficient, pricing adjustments are desired. Customers often aim to keep monthly spending near $100,000.

"On average, customers pay about $300,000 USD per month."
"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."

Popular Use Cases

Amazon SageMaker is primarily used for machine learning model development, training, deployment, and predictive analysis. Users apply it in various domains like demand forecasting, image recognition, and NLP projects. Many utilize SageMaker for migrating to cloud-based AI infrastructures, leveraging its compute resources for complex tasks, and managing end-to-end machine learning pipelines. Several organizations appreciate its capabilities for enhancing data processing, model hosting, and providing off-the-shelf solutions for different business needs.

Service and Support

Amazon SageMaker support is generally positive, with many users satisfied. Some leverage documentation effectively, minimizing the need for external help. Premium AWS customers report better service. Response times can vary, with some instances of delays. Support quality is rated between 5 to 10 out of 10 in different experiences. Technical support through chat or calls is effective, though slower response and additional costs for enhanced service are noted. In-house and partner support are appreciated by some.

Deployment

Amazon SageMaker's initial setup was often described as straightforward and easy. Many noted that it could be completed quickly, typically within 15 to 20 minutes, although some complexities arose depending on specific use cases or integration needs. Users appreciated the cloud-native aspect, which eliminated manual installation. However, beginners or those unfamiliar with AWS might encounter challenges, with ease ratings varying. Proper AWS knowledge significantly facilitated setup, while additional customization or integration took more time.

Scalability

Amazon SageMaker is highly scalable, rated positively by many users. While it supports small to medium data volumes efficiently, some challenges can arise with larger datasets. Users commend its flexible compute resources and integration with AWS infrastructure, though setup can be complex. Businesses across various scales utilize it, noting that its scalability is contingent on user expertise and AWS's provisioning. Some find it expensive for extensive ML projects, but it's generally regarded as powerful for scalability.

Stability

Amazon SageMaker is largely stable, with users noting minimal issues. The platform's reliability is backed by AWS infrastructure, praised for being serverless and robust. While configuration can impact stability, proper setup minimizes risks. Most users rate the stability between eight to nine out of ten. There are occasional downtimes, yet they seldom affect daily activities significantly. Users appreciate its performance, especially when paired with Kubernetes clusters, though comments highlight room for scalability and user experience improvements.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

Download our Amazon SageMaker Buyer's Guide for additional reliable information.