Amazon SageMaker Reviews

Name: Amazon SageMaker
Brand: Amazon Web Services (AWS)
Rating: 3.9 (32 reviews)

Vendor: Amazon Web Services (AWS)

3.9 out of 5

32 reviews
90% willing to recommend

1,272 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 32 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 65% 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 823,795 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 17, 2024	Download
Product	Reviews, tips, and advice from real users	Dec 17, 2024	Download
Comparison	Amazon SageMaker vs Databricks	Dec 17, 2024	Download
Comparison	Amazon SageMaker vs KNIME	Dec 17, 2024	Download
Comparison	Amazon SageMaker vs Microsoft Azure Machine Learning Studio	Dec 17, 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 offers Random Cut Forest, IDE, pre-built solutions like AlexNet, and flexibility in resources. Users benefit from seamless AWS integration, ease of use, API endpoints, data catalog, and model deployment. Hyperparameter tuning, enriched libraries, and AutoML provide efficiency. Its capacity for model monitoring, serverless computing, and scalability stands out. SageMaker Studio enhances workflows. It integrates well with various AWS tools, simplifying machine learning processes while providing a rich ecosystem for model management.

"I appreciate the ease of use in Amazon SageMaker."
"One of the most valuable features of Amazon SageMaker for me is the one-touch deployment, which simplifies the process greatly."
"The most valuable features in Amazon SageMaker are its AutoML, feature store, and automated hyperparameter tuning capabilities."

Room for Improvement

Amazon SageMaker requires simpler pricing, better user interface, and enhanced documentation. Its training programs and data integration processes are complex, demanding more intuitive solutions. High costs, especially for GPU usage, are a concern. Security enhancements and improved model repository management are needed. End-to-end templates and smoother integration with tools like Snowflake can enhance user experience. Expanding AI functionalities and offering free slots for practice would support beginners in exploring machine learning on SageMaker.

"I would recommend having more walkthrough videos and articles beyond AWS Skill Builder."
"The dashboard could be improved by including more features and providing more information about deployed models, their drift, performance, scaling, and customization options."
"Improvements are needed in terms of complexity, data security, and access policy integration in Amazon SageMaker."

ROI

Organizations have noticed an ROI increase with Amazon SageMaker. Its effectiveness varies based on specific use cases. It is efficient for those already running on AWS, saving time by quickly initiating AI models. Cost reductions are achieved through its pre-configured components, enhancing the ML lifecycle. In one instance, combining SageMaker with Pawsit resulted in savings of about three million dollars. However, the long-term cost can be high for regular use, impacting ongoing adoption.

Pricing

Amazon SageMaker pricing is complex and largely based on compute resources, storage, and configuration options. Costs include compute instance usage and optional business support services, and while no additional licenses are required, expenses can be high. Users find pricing competitive compared to other cloud services, yet often mention the solution's overall expense. Monthly costs can vary from thousands to $300,000, depending on usage and scale. Customers frequently seek pricing improvements and more transparent cost-impacting configurations.

"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

Organizations primarily use Amazon SageMaker for deploying and developing machine learning models. They leverage its capabilities for various tasks such as predictive modeling, demand forecasting, outlier detection, and data analysis. Companies utilize SageMaker's cloud-based infrastructure to handle large datasets, automate workflows, and deploy models efficiently, thus saving on infrastructure costs. It is also used for specific applications like computer vision, OCR, and customer behavior analysis, benefiting from its flexibility and powerful compute resources.

Service and Support

Amazon SageMaker users express varied opinions on customer service and support. While many find the technical support effective and rate it highly, others experience slow response times. Premium AWS customers enjoy 24/7 service with faster assistance. Some do not require much support due to comprehensive documentation or in-house expertise. Support's quality is often determined by user status, with higher satisfaction reported by those accessing premium services. Numerous users appreciate training and technical assistance from local teams.

Deployment

Amazon SageMaker's initial setup is generally straightforward, taking as little as 15 minutes with the right expertise. Users find it easy if they are familiar with AWS, though complexities arise with custom configurations. Setups involving cloud-native deployments or integrations with platforms like Jenkins are quick. Rating on ease varies, from simple for those with a machine learning background to challenging for beginners. The process often involves collaboration between data scientists and engineers.

Scalability

Amazon SageMaker is highly scalable according to user feedback. It effectively meets project requirements and accommodates various company sizes, from small teams to large organizations. While some users mention its excellent adaptability and capacity to handle substantial data, others note a need for improved scalability with larger datasets. Skill levels in deployment can affect scalability experiences. Typically, it offers flexible resource allocation and dynamic scaling, making it suited for diverse users, rating it highly in scalability.