Amazon SageMaker
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.
SAS Viya
SAS Viya is a cloud-native data management and analytics platform that helps you build, deploy, and manage artificial intelligence and machine learning models to solve complex business problems efficiently.
Quick Comparison
| Feature | Amazon SageMaker | SAS Viya |
|---|---|---|
| Website | aws.amazon.com | sas.com |
| Pricing Model | Subscription | Custom |
| Starting Price | Free | Custom Pricing |
| FREE Trial | ✓ 60 days free trial | ✓ 14 days free trial |
| Free Plan | ✘ No free plan | ✘ No free plan |
| Product Demo | ✓ Request demo here | ✓ Request demo here |
| Deployment | ||
| Integrations | ||
| Target Users | ||
| Target Industries | ||
| Customer Count | 0 | 0 |
| Founded Year | 2017 | 1976 |
| Headquarters | Seattle, USA | Cary, USA |
Overview
Amazon SageMaker
Amazon SageMaker is a comprehensive hub where you can build, train, and deploy machine learning models at scale. It removes the heavy lifting from each step of the machine learning process, allowing you to focus on your data and logic rather than managing underlying infrastructure. You can use integrated Jupyter notebooks for easy access to your data sources for exploration and analysis without servers to manage.
The platform provides specific modules for every stage of the lifecycle, from data labeling with Ground Truth to automated model building with Autopilot. You can deploy your finished models into production with a single click, and the system automatically scales to handle your traffic. Whether you are a solo data scientist or part of a large enterprise team, you can reduce your development time and costs significantly by using these purpose-built tools.
SAS Viya
SAS Viya is a cloud-native analytics platform designed to help you manage the entire data lifecycle in one place. You can move from raw data to production-ready AI models using a unified interface that supports both visual drag-and-drop tools and popular programming languages like Python and R. This flexibility allows your data scientists and business analysts to collaborate effectively on the same projects.
The platform handles massive datasets with ease thanks to its distributed, in-memory processing engine. You can deploy it on any cloud provider or on-premises environment to maintain control over your infrastructure. By automating repetitive data preparation and model tuning tasks, you can focus on uncovering insights that drive better business decisions and operational efficiency across your entire organization.
Overview
Amazon SageMaker Features
- SageMaker Studio Access a single web-based visual interface where you can perform all machine learning development steps in one place.
- Autopilot Build and train the best machine learning models automatically based on your data while maintaining full visibility and control.
- Data Wrangler Import, transform, and analyze your data quickly using over 300 built-in data transformations without writing any code.
- Ground Truth Build highly accurate training datasets for machine learning using managed human labeling services or automated data labeling.
- Model Monitor Detect deviations in model quality automatically so you can maintain high accuracy for your predictions over time.
- Clarify Improve your model transparency by detecting potential bias and explaining how specific features contribute to your model's predictions.
SAS Viya Features
- Visual Data Preparation. Clean and transform your data using an intuitive visual interface that automatically generates code in the background.
- Automated Machine Learning. Build high-quality models faster by letting the system automatically handle feature engineering, algorithm selection, and hyperparameter tuning.
- Model Management. Register, track, and monitor all your analytical models in a central repository to ensure they perform reliably over time.
- Open Language Support. Write code in Python, R, or Lua while still taking advantage of the platform's powerful distributed processing capabilities.
- Real-Time Decisioning. Embed your analytical models directly into business processes to trigger automated actions and responses the moment data arrives.
- Natural Language Generation. Receive automated text explanations of your data visualizations so you can understand the 'why' behind your business trends.
Pricing Comparison
Amazon SageMaker Pricing
- 250 hours of Studio Notebooks
- 50 hours of m5.explainer instances
- 10 million characters for Clarify
- First 2 months included
- Data Wrangler 25 hours/month
- Everything in Free Tier, plus:
- Pay-as-you-go compute instances
- No upfront commitments
- Per-second billing for usage
- Choice of GPU or CPU instances
- Scale storage independently
SAS Viya Pricing
Pros & Cons
Amazon SageMaker
Pros
- Eliminates the need to manage complex server infrastructure
- Integrates perfectly with other AWS data services
- Speeds up the deployment of models to production
- Supports all major machine learning frameworks like TensorFlow
- Automates repetitive data labeling and cleaning tasks
Cons
- Learning curve can be steep for AWS beginners
- Costs can escalate quickly without careful monitoring
- Documentation is extensive but sometimes difficult to navigate
SAS Viya
Pros
- Handles extremely large datasets with high performance
- Unified environment for both coding and visual users
- Strong governance features for regulated industries
- Excellent technical support and extensive documentation
- Seamless integration with major cloud providers
Cons
- Higher price point compared to open-source alternatives
- Significant learning curve for the full feature set
- Initial setup and configuration can be complex