Amazon SageMaker Features
What are the features of Amazon SageMaker?
Model Development
- Language Support
- Drag and Drop
- Pre-Built Algorithms
- Model Training
- Pre-Built Algorithms
- Model Training
- Feature Engineering
Machine/Deep Learning Services
- Computer Vision
- Natural Language Processing
- Natural Language Generation
- Artificial Neural Networks
Deployment
- Managed Service
- Application
- Scalability
System
- Data Ingestion & Wrangling
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Model Development
Language Support | Based on 25 Amazon SageMaker reviews. Supports programming languages such as Java, C, or Python. Supports front-end languages such as HTML, CSS, and JavaScript | 89% (Based on 25 reviews) | |
Drag and Drop | Offers the ability for developers to drag and drop pieces of code or algorithms when building models 24 reviewers of Amazon SageMaker have provided feedback on this feature. | 83% (Based on 24 reviews) | |
Pre-Built Algorithms | Provides users with pre-built algorithms for simpler model development 29 reviewers of Amazon SageMaker have provided feedback on this feature. | 84% (Based on 29 reviews) | |
Model Training | Supplies large data sets for training individual models 29 reviewers of Amazon SageMaker have provided feedback on this feature. | 89% (Based on 29 reviews) | |
Pre-Built Algorithms | Provides users with pre-built algorithms for simpler model development 15 reviewers of Amazon SageMaker have provided feedback on this feature. | 86% (Based on 15 reviews) | |
Model Training | As reported in 15 Amazon SageMaker reviews. Supplies large data sets for training individual models | 89% (Based on 15 reviews) | |
Feature Engineering | Transforms raw data into features that better represent the underlying problem to the predictive models This feature was mentioned in 15 Amazon SageMaker reviews. | 86% (Based on 15 reviews) |
Machine/Deep Learning Services
Computer Vision | Offers image recognition services 22 reviewers of Amazon SageMaker have provided feedback on this feature. | 92% (Based on 22 reviews) | |
Natural Language Processing | Offers natural language processing services This feature was mentioned in 24 Amazon SageMaker reviews. | 90% (Based on 24 reviews) | |
Natural Language Generation | Based on 21 Amazon SageMaker reviews. Offers natural language generation services | 88% (Based on 21 reviews) | |
Artificial Neural Networks | As reported in 24 Amazon SageMaker reviews. Offers artificial neural networks for users | 90% (Based on 24 reviews) | |
Computer Vision | As reported in 12 Amazon SageMaker reviews. Offers image recognition services | 96% (Based on 12 reviews) | |
Natural Language Understanding | Offers natural language understanding services This feature was mentioned in 13 Amazon SageMaker reviews. | 92% (Based on 13 reviews) | |
Natural Language Generation | Offers natural language generation services This feature was mentioned in 13 Amazon SageMaker reviews. | 90% (Based on 13 reviews) | |
Deep Learning | As reported in 14 Amazon SageMaker reviews. Provides deep learning capabilities | 90% (Based on 14 reviews) |
Deployment
Managed Service | Manages the intelligent application for the user, reducing the need of infrastructure 28 reviewers of Amazon SageMaker have provided feedback on this feature. | 88% (Based on 28 reviews) | |
Application | As reported in 28 Amazon SageMaker reviews. Allows users to insert machine learning into operating applications | 86% (Based on 28 reviews) | |
Scalability | As reported in 27 Amazon SageMaker reviews. Provides easily scaled machine learning applications and infrastructure | 90% (Based on 27 reviews) | |
Language Flexibility | Allows users to input models built in a variety of languages. | Not enough data | |
Framework Flexibility | Allows users to choose the framework or workbench of their preference. | Not enough data | |
Versioning | Records versioning as models are iterated upon. | Not enough data | |
Ease of Deployment | Provides a way to quickly and efficiently deploy machine learning models. | Not enough data | |
Scalability | Offers a way to scale the use of machine learning models across an enterprise. | Not enough data | |
Managed Service | Based on 14 Amazon SageMaker reviews. Manages the intelligent application for the user, reducing the need of infrastructure | 95% (Based on 14 reviews) | |
Application | Allows users to insert machine learning into operating applications 14 reviewers of Amazon SageMaker have provided feedback on this feature. | 88% (Based on 14 reviews) | |
Scalability | Provides easily scaled machine learning applications and infrastructure 13 reviewers of Amazon SageMaker have provided feedback on this feature. | 97% (Based on 13 reviews) | |
Language Flexibility | Allows users to input models built in a variety of languages. | Not enough data | |
Framework Flexibility | Allows users to choose the framework or workbench of their preference. | Not enough data | |
Versioning | Records versioning as models are iterated upon. | Not enough data | |
Ease of Deployment | Provides a way to quickly and efficiently deploy machine learning models. | Not enough data | |
Scalability | Offers a way to scale the use of machine learning models across an enterprise. | Not enough data |
Management
Cataloging | Records and organizes all machine learning models that have been deployed across the business. | Not enough data | |
Monitoring | Tracks the performance and accuracy of machine learning models. | Not enough data | |
Governing | Provisions users based on authorization to both deploy and iterate upon machine learning models. | Not enough data | |
Model Registry | Allows users to manage model artifacts and tracks which models are deployed in production. | Not enough data | |
Cataloging | Records and organizes all machine learning models that have been deployed across the business. | Not enough data | |
Monitoring | Tracks the performance and accuracy of machine learning models. | Not enough data | |
Governing | Provisions users based on authorization to both deploy and iterate upon machine learning models. | Not enough data |
System
Data Ingestion & Wrangling | Gives user ability to import a variety of data sources for immediate use This feature was mentioned in 15 Amazon SageMaker reviews. | 81% (Based on 15 reviews) | |
Language Support | As reported in 13 Amazon SageMaker reviews. Supports programming languages such as Java, C, or Python. Supports front-end languages such as HTML, CSS, and JavaScript | 88% (Based on 13 reviews) | |
Drag and Drop | Offers the ability for developers to drag and drop pieces of code or algorithms when building models 12 reviewers of Amazon SageMaker have provided feedback on this feature. | 90% (Based on 12 reviews) |
Operations
Metrics | Control model usage and performance in production | Not enough data | |
Infrastructure management | Deploy mission-critical ML applications where and when you need them | Not enough data | |
Collaboration | Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance. | Not enough data |
