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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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Amazon SageMaker Categories on G2
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Model Development
Language Support | Supports programming languages such as Java, C, or Python. Supports front-end languages such as HTML, CSS, and JavaScript 28 reviewers of Amazon SageMaker have provided feedback on this feature. | 89% (Based on 28 reviews) | |
Drag and Drop | Offers the ability for developers to drag and drop pieces of code or algorithms when building models This feature was mentioned in 27 Amazon SageMaker reviews. | 82% (Based on 27 reviews) | |
Pre-Built Algorithms | Provides users with pre-built algorithms for simpler model development 32 reviewers of Amazon SageMaker have provided feedback on this feature. | 84% (Based on 32 reviews) | |
Model Training | Based on 32 Amazon SageMaker reviews. Supplies large data sets for training individual models | 89% (Based on 32 reviews) | |
Pre-Built Algorithms | As reported in 18 Amazon SageMaker reviews. Provides users with pre-built algorithms for simpler model development | 86% (Based on 18 reviews) | |
Model Training | Supplies large data sets for training individual models This feature was mentioned in 18 Amazon SageMaker reviews. | 88% (Based on 18 reviews) | |
Feature Engineering | Transforms raw data into features that better represent the underlying problem to the predictive models This feature was mentioned in 18 Amazon SageMaker reviews. | 84% (Based on 18 reviews) |
Machine/Deep Learning Services
Computer Vision | Offers image recognition services 25 reviewers of Amazon SageMaker have provided feedback on this feature. | 89% (Based on 25 reviews) | |
Natural Language Processing | As reported in 27 Amazon SageMaker reviews. Offers natural language processing services | 91% (Based on 27 reviews) | |
Natural Language Generation | Based on 24 Amazon SageMaker reviews. Offers natural language generation services | 89% (Based on 24 reviews) | |
Artificial Neural Networks | As reported in 27 Amazon SageMaker reviews. Offers artificial neural networks for users | 90% (Based on 27 reviews) | |
Computer Vision | As reported in 15 Amazon SageMaker reviews. Offers image recognition services | 97% (Based on 15 reviews) | |
Natural Language Understanding | Based on 16 Amazon SageMaker reviews. Offers natural language understanding services | 92% (Based on 16 reviews) | |
Natural Language Generation | Based on 16 Amazon SageMaker reviews. Offers natural language generation services | 90% (Based on 16 reviews) | |
Deep Learning | Provides deep learning capabilities 17 reviewers of Amazon SageMaker have provided feedback on this feature. | 91% (Based on 17 reviews) |
Deployment
Managed Service | Manages the intelligent application for the user, reducing the need of infrastructure 31 reviewers of Amazon SageMaker have provided feedback on this feature. | 87% (Based on 31 reviews) | |
Application | Allows users to insert machine learning into operating applications 31 reviewers of Amazon SageMaker have provided feedback on this feature. | 86% (Based on 31 reviews) | |
Scalability | Provides easily scaled machine learning applications and infrastructure This feature was mentioned in 30 Amazon SageMaker reviews. | 91% (Based on 30 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 | As reported in 17 Amazon SageMaker reviews. Manages the intelligent application for the user, reducing the need of infrastructure | 91% (Based on 17 reviews) | |
Application | Allows users to insert machine learning into operating applications This feature was mentioned in 17 Amazon SageMaker reviews. | 85% (Based on 17 reviews) | |
Scalability | Provides easily scaled machine learning applications and infrastructure This feature was mentioned in 16 Amazon SageMaker reviews. | 92% (Based on 16 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 18 reviewers of Amazon SageMaker have provided feedback on this feature. | 81% (Based on 18 reviews) | |
Language Support | Supports programming languages such as Java, C, or Python. Supports front-end languages such as HTML, CSS, and JavaScript 16 reviewers of Amazon SageMaker have provided feedback on this feature. | 89% (Based on 16 reviews) | |
Drag and Drop | As reported in 15 Amazon SageMaker reviews. Offers the ability for developers to drag and drop pieces of code or algorithms when building models | 91% (Based on 15 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 |
Generative AI
AI Text Generation | Allows users to generate text based on a text prompt. | Not enough data | |
AI Text Summarization | Condenses long documents or text into a brief summary. | Not enough data | |
AI Text Generation | Allows users to generate text based on a text prompt. | Not enough data | |
AI Text Summarization | Condenses long documents or text into a brief summary. | Not enough data | |
AI Text-to-Image | Provides the ability to generate images from a text prompt. | Not enough data |
Scalability and Performance - Generative AI Infrastructure
AI High Availability | Ensures that the service is reliable and available when needed, minimizing downtime and service interruptions. | Not enough data | |
AI Model Training Scalability | Allows the user to scale the training of models efficiently, making it easier to deal with larger datasets and more complex models. | Not enough data | |
AI Inference Speed | Provides the user the ability to get quick and low-latency responses during the inference stage, which is critical for real-time applications. | Not enough data |
Cost and Efficiency - Generative AI Infrastructure
AI Cost per API Call | Offers the user a transparent pricing model for API calls, enabling better budget planning and cost control. | Not enough data | |
AI Resource Allocation Flexibility | Provides the user the ability to allocate computational resources based on demand, making it cost-effective. | Not enough data | |
AI Energy Efficiency | Allows the user to minimize energy usage during both training and inference, which is becoming increasingly important for sustainable operations. | Not enough data |
Integration and Extensibility - Generative AI Infrastructure
AI Multi-cloud Support | Offers the user the flexibility to deploy across multiple cloud providers, reducing the risk of vendor lock-in. | Not enough data | |
AI Data Pipeline Integration | Provides the user the ability to seamlessly connect with various data sources and pipelines, simplifying data ingestion and pre-processing. | Not enough data | |
AI API Support and Flexibility | Allows the user to easily integrate the generative AI models into existing workflows and systems via APIs. | Not enough data |
Security and Compliance - Generative AI Infrastructure
AI GDPR and Regulatory Compliance | Helps the user maintain compliance with GDPR and other data protection regulations, which is crucial for businesses operating globally. | Not enough data | |
AI Role-based Access Control | Allows the user to set up access controls based on roles within the organization, enhancing security. | Not enough data | |
AI Data Encryption | Ensures that data is encrypted during transit and at rest, providing an additional layer of security. | Not enough data |
Usability and Support - Generative AI Infrastructure
AI Documentation Quality | Provides the user with comprehensive and clear documentation, aiding in quicker adoption and troubleshooting. | Not enough data | |
AI Community Activity | Allows the user to gauge the level of community support and third-party extensions available, which can be useful for problem-solving and extending functionality. | Not enough data |
Agentic AI - Data Science and Machine Learning Platforms
Autonomous Task Execution | Capability to perform complex tasks without constant human input | Not enough data | |
Multi-step Planning | Ability to break down and plan multi-step processes | Not enough data | |
Cross-system Integration | Works across multiple software systems or databases | Not enough data | |
Adaptive Learning | Improves performance based on feedback and experience | Not enough data | |
Natural Language Interaction | Engages in human-like conversation for task delegation | Not enough data | |
Proactive Assistance | Anticipates needs and offers suggestions without prompting | Not enough data | |
Decision Making | Makes informed choices based on available data and objectives | Not enough data |
