What is Amazon SageMaker?
Bringing together widely adopted AWS machine learning (ML) and analytics capabilities, Amazon SageMaker delivers an integrated experience for analytics and AI with unified access to all your data. Collaborate and build faster from a unified studio (preview) using familiar AWS tools for model development, generative AI, data processing, and SQL analytics, accelerated by Amazon Q Developer, the most capable generative AI assistant for software development.
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Get AssistanceAmazon SageMaker Ratings
Real user data aggregated to summarize the product performance and customer experience.
Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.
87 Likeliness to Recommend
100 Plan to Renew
85 Satisfaction of Cost Relative to Value
Emotional Footprint Overview
Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.
+98 Net Emotional Footprint
The emotional sentiment held by end users of the software based on their experience with the vendor. Responses are captured on an eight-point scale.
How much do users love Amazon SageMaker?
Pros
- Continually Improving Product
- Reliable
- Enables Productivity
- Unique Features
How to read the Emotional Footprint
The Net Emotional Footprint measures high-level user sentiment towards particular product offerings. It aggregates emotional response ratings for various dimensions of the vendor-client relationship and product effectiveness, creating a powerful indicator of overall user feeling toward the vendor and product.
While purchasing decisions shouldn't be based on emotion, it's valuable to know what kind of emotional response the vendor you're considering elicits from their users.
Footprint
Negative
Neutral
Positive
Feature Ratings
Performance and Scalability
Data Labeling
Feature Engineering
Ensembling
Data Pre-Processing
Model Monitoring and Management
Model Training
Algorithm Diversity
Algorithm Recommendation
Model Tuning
Explainability
Vendor Capability Ratings
Ease of Customization
Vendor Support
Ease of Data Integration
Breadth of Features
Ease of IT Administration
Ease of Implementation
Availability and Quality of Training
Quality of Features
Business Value Created
Usability and Intuitiveness
Product Strategy and Rate of Improvement
Amazon SageMaker Reviews
Jefferson A.
- Role: Industry Specific Role
- Industry: Engineering
- Involvement: IT Development, Integration, and Administration
Submitted Jan 2025
Powerful and versatile, but can be costly
Likeliness to Recommend
What differentiates Amazon SageMaker from other similar products?
Amazon SageMaker stands out for its end-to-end machine learning workflow, offering tools for data preparation, model training, tuning, deployment, and monitoring all within a unified platform. Its seamless integration with other AWS services, like S3 and Lambda, enables efficient handling of large-scale simulations and data-intensive models, making it highly scalable and versatile for diverse simulation needs.
What is your favorite aspect of this product?
My favorite aspect of Amazon SageMaker is its managed infrastructure for training and deploying models, which eliminates the need for manual resource management. This allows me to focus entirely on building and refining simulation models while benefiting from seamless scaling and integration with other AWS services.
What do you dislike most about this product?
What I dislike most about Amazon SageMaker is its high cost for large-scale simulations and long-running projects, especially when leveraging advanced features like distributed training and endpoint hosting. Additionally, the platform's complex pricing structure can make it challenging to predict and manage expenses effectively.
What recommendations would you give to someone considering this product?
Here are my recommendations: Evaluate Your Budget: Be mindful of the costs, especially when working with large datasets or running complex simulations. Consider using cost estimation tools to predict expenses and optimize resource usage. Leverage Managed Services: Take advantage of SageMaker’s managed infrastructure to simplify the training, deployment, and scaling of models, which will save time and effort. Start Small: Begin with smaller projects to get familiar with the platform and its capabilities before scaling up to more complex simulations or machine learning models.
Pros
- Helps Innovate
- Continually Improving Product
- Reliable
- Performance Enhancing
