SageMaker vs SAS Viya

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Our analysts compared SageMaker vs SAS Viya based on data from our 400+ point analysis of Big Data Analytics Tools, user reviews and our own crowdsourced data from our free software selection platform.

SageMaker Software Tool

Product Basics

Amazon SageMaker is a comprehensive machine learning platform by Amazon Web Services (AWS) designed to simplify the entire machine learning lifecycle. It empowers businesses to build, train, deploy, and manage machine learning models efficiently. Key features include robust data preprocessing tools, a wide selection of machine learning algorithms, and automated hyperparameter tuning. SageMaker's scalability ensures it's suitable for both small experiments and large-scale production deployments. It offers cost-efficiency with a pay-as-you-go pricing model and facilitates model management and monitoring. The platform integrates seamlessly with the AWS ecosystem, providing security and compliance features. SageMaker's AutoML capabilities make machine learning accessible to users of varying expertise. Overall, it streamlines the machine learning process, enabling organizations to harness the power of AI for improved decision-making and innovation.
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SAS Viya is a cloud-based in-memory analytics engine that provides data visualization, reporting and analytics to businesses for actionable data insights. Powered by AI, it brings together visual analytics, visual statistics and data science for enterprises to achieve end-to-end self-service analytics. It uses a standardized code base with support for programming in R, Python, SAS, Java and Lua.

Deployable in the cloud, on-premises and hybrid environments, it integrates with a wide range of business applications through an agile, scalable architecture. The vendor offers an introductory 30-day free trial.

Pros
  • Comprehensive features
  • Powerful analytics capabilities
  • User-friendly interface
  • Scalable architecture
  • Strong support from SAS
Cons
  • Steep learning curve
  • Limited customization options
  • High cost of ownership
  • Potential vendor lock-in
  • Resource-intensive
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$0.51 Hourly
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$10,000 Annual
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Product Assistance

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Product Insights

  • Accelerated Machine Learning: Amazon SageMaker offers a robust environment for building, training, and deploying machine learning models quickly and efficiently. It streamlines the ML workflow, reducing time-to-market.
  • Scalability: With SageMaker, you can effortlessly scale your machine learning projects. It can handle both small-scale experiments and large-scale production deployments, ensuring flexibility as your needs evolve.
  • Cost Efficiency: SageMaker's pay-as-you-go pricing model and built-in cost optimization tools help you manage expenses effectively. It optimizes resource allocation, preventing unnecessary spending.
  • Managed Infrastructure: The service abstracts the complexities of infrastructure management. This allows data scientists and developers to focus on model development rather than worrying about provisioning and maintaining infrastructure.
  • AutoML Capabilities: SageMaker provides AutoML features that automate aspects of model selection, hyperparameter tuning, and deployment, making it accessible to users with varying levels of expertise.
  • Robust Data Labeling: SageMaker includes data labeling tools and integration with Amazon Mechanical Turk, making it easier to annotate and prepare data for training, a critical step in machine learning workflows.
  • Secure and Compliant: Amazon SageMaker adheres to industry-leading security and compliance standards. It encrypts data, monitors access, and offers tools for compliance with regulations like GDPR and HIPAA.
  • Customizable Workflows: SageMaker's flexibility allows you to customize your machine learning workflows to suit your specific requirements. You can integrate your own algorithms, libraries, and tools seamlessly.
  • Model Management: It simplifies model management, versioning, and deployment, making it easy to keep track of different iterations of your models and roll out updates effortlessly.
  • Real-time Inference: SageMaker supports real-time model inference, enabling you to integrate machine learning predictions into your applications and services in real-time, enhancing user experiences.
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  • Improve Decision-Making: Make informed business decisions by using historical and current proprietary information to derive analytical insights. Compute vast amounts of data faster and resolve complexities through parallel processing. Boost workflow efficiencies by deploying operational decisions that define real-time best actions at scale. 
  • Self-Service Analytics: Easily perform automated forecasting, goal-seeking and scenario analysis — no technical skills needed. Identify user sentiment through text analytics and incorporate geographical data for a complete picture of business metrics. 
  • Maximize ROI: Save time through built-in automation for data prep, feature engineering, algorithm selection and AI-powered data discovery. Innovate, rather than spending time on tedious data management and analytics tasks. 
  • Data Security: Ensure data encryption at rest and while moving across systems, in addition to auditing protocols. Connect to external data management systems like Oracle, Teradata, Facebook, Amazon and Esri seamlessly through Kerberos, SAML, OAuth and OpenID. 
  • Data Management: Import data by using an IDE or through REST APIs, and visualize and analyze it through self-service data prep. Join tables, apply functions and perform calculations, or drag-and-drop, pivot, and slice and dice to view desired metrics. 
  • Augmented Analytics: Identify relationships in data through automatically generated suggestions and guided analysis, and track anomalies and outliers. Tell data stories by generating easy-to-understand visuals and dashboard summaries in natural language. Derive meaningful insights for the future by creating what-if scenarios for forecasting and predictive analytics. 
  • Mobility: Access business reports on the go through a native mobile app that supports a variety of charts, graphs and tables. Configure app functionality per device for specific users to add and view reports, share links, add and view comments and view alerts. 
  • Scalable Architecture: Leverage its modular microservices architecture to scale as per business needs. Monitor and manage the health and configuration of individual microservices instances through the SAS Environment Manager. Deploys seamlessly to any type of environment, including the cloud, and runs on Cloud Foundry as a platform-as-a-service (PaaS). 
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  • Data Preprocessing Tools: SageMaker offers a range of data preprocessing capabilities, including data cleaning, transformation, and feature engineering, enabling users to prepare data efficiently for machine learning.
  • Wide Model Selection: Users have access to a diverse library of machine learning algorithms, from linear regression to deep learning frameworks like TensorFlow, making it suitable for a variety of use cases.
  • Hyperparameter Tuning: SageMaker automates hyperparameter optimization, helping users find the best configurations for their models, which can significantly improve model performance.
  • Model Training at Scale: It supports distributed training across multiple instances, reducing training times and enabling the handling of large datasets with ease.
  • Model Deployment: Users can deploy models as RESTful APIs, facilitating real-time inference in applications and services, and manage multiple model versions seamlessly.
  • AutoML Capabilities: SageMaker Autopilot streamlines model creation for users without deep machine learning expertise, automating tasks like feature engineering and model selection.
  • Monitoring and Debugging: It offers tools for model monitoring and debugging, helping users detect and address issues in deployed models, ensuring reliability in production.
  • Explainability and Bias Detection: SageMaker provides features for model explainability and bias detection, essential for understanding model predictions and addressing ethical considerations.
  • Integration with AWS Ecosystem: Seamlessly integrates with other AWS services, such as S3, Lambda, and Step Functions, facilitating end-to-end machine learning workflows within the AWS environment.
  • Security and Compliance: Offers comprehensive security features, including data encryption, access control, and compliance with industry standards, making it suitable for sensitive industries like healthcare and finance.
  • Cost Optimization: SageMaker includes cost optimization tools like automatic model scaling, enabling users to manage and optimize machine learning expenses efficiently.
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  • Visualization and Reporting: Dig into data for in-depth analysis and view key business metrics through autonomous data exploration and manipulation. Create and customize interactive reports and charts to share with others across the organization for collaborative insight. Get suggestions on graphics best suited to display pertinent data through auto charting. 
  • Data Modeling: Analyze data with predictive models through regression, clustering and neural networks. Ensure version control by tracking data models from creation through usage by registering, validating and monitoring each version. Creates snapshots of model properties and files and retains them for the future. 
  • Visual Statistics: Build diverse scenarios simultaneously and refine them with what-if analyses to uncover insights through experimentation. Unifies all business tools, irrespective of the language they support, into a common visual analytics solution. 
  • Cloud Integrations: Develop low-code technologies by porting SAS open-source models into mobile and business applications through its cloud-native capability. Optimize analytics workloads on clouds like Microsoft Azure and ensure cost-efficient migration of analytics to the cloud through a workload management tool. 
  • ML-Based Insights: Get valuable insights from new data types by combining structured and unstructured data in integrated machine learning programs. Choose the desired ML algorithm from a range of options and easily find the optimal parameter settings. Use Python within Jupyter notebooks for deep learning functions like computer vision, natural language processing, forecasting and speech processing. 
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Product Ranking

#28

among all
Big Data Analytics Tools

#41

among all
Big Data Analytics Tools

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Analyst Rating Summary

84
94
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96
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73
98
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Availability and Scalability
Platform Security
Machine Learning
Integrations and Extensibility
Availability and Scalability
Computer Vision and Internet of Things (IoT)
Platform Security
Dashboarding and Data Visualization
Augmented Analytics

Analyst Ratings for Functional Requirements Customize This Data Customize This Data

SageMaker
SAS Viya
+ Add Product + Add Product
Augmented Analytics Computer Vision And Internet Of Things (IoT) Dashboarding And Data Visualization Data Management Data Preparation Geospatial Visualizations And Analysis Machine Learning Mobile Capabilities Platform Capabilities Reporting 84 84 73 76 81 89 0 63 96 100 98 96 86 93 70 96 0 25 50 75 100
83%
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63%
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71%
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29%
86%
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86%
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14%
86%
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87%
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63%
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37%
83%
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17%
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29%
57%
14%
86%
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Analyst Ratings for Technical Requirements Customize This Data Customize This Data

100%
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82%
4%
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82%
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18%
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User Sentiment Summary

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Great User Sentiment 203 reviews
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85%
of users recommend this product

SAS Viya has a 'great' User Satisfaction Rating of 85% when considering 203 user reviews from 3 recognized software review sites.

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4.2 (157)
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4.3 (33)
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4.7 (13)

Awards

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SelectHub research analysts have evaluated SAS Viya and concluded it deserves the award for the Best Overall Big Data Analytics Tools available today and earns best-in-class honors for Augmented Analytics and Computer Vision and Internet of Things (IoT).

Analysts' Pick Award
Augmented Analytics Award
Computer Vision and Internet of Things (IoT) Award

Synopsis of User Ratings and Reviews

Robust Feature Set: Users appreciate SageMaker's comprehensive feature set, which covers data preprocessing, model training, deployment, and monitoring, all in one platform.
Scalability: Many users highlight SageMaker's ability to scale seamlessly, accommodating both small-scale experiments and large-scale production workloads.
Cost-Efficiency: The pay-as-you-go pricing model and cost optimization tools receive positive reviews for helping users manage machine learning expenses effectively.
Integration with AWS: Users value SageMaker's integration with the broader AWS ecosystem, simplifying workflows and enhancing compatibility with other AWS services.
AutoML Capabilities: SageMaker's AutoML features, such as Autopilot, receive praise for automating complex machine learning tasks, making it accessible to a broader range of users.
Model Management: Users find the platform's model versioning and management tools useful for keeping track of models and deploying updates efficiently.
Security and Compliance: The robust security features, including data encryption and compliance with industry standards, are seen as a critical advantage for users with stringent data security requirements.
Real-time Inference: Users appreciate the capability to deploy models as RESTful APIs, enabling real-time predictions in applications and services, enhancing user experiences.
Community Support: Some users highlight the active SageMaker community, which provides valuable resources, tutorials, and support for users at all skill levels.
Extensive Documentation: Users find the platform's extensive documentation and tutorials helpful for onboarding and troubleshooting, contributing to a smoother user experience.
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Ease of Use: All users who mention its interface say that it makes autonomous analysis and data modeling accessible to users of all skill levels.
Support: All users who review support say that representatives are responsive and helpful in resolving issues and queries.
Functionality: Around 71% of the users who comment on its feature set say that the software helps discover data insights through powerful visualizations and on-the-fly calculations.
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Complex Learning Curve: Users often find SageMaker challenging for beginners due to its extensive feature set, requiring significant time and effort to master.
Cost Management: Some users report difficulty in managing costs effectively, especially during large-scale model training, which can lead to unexpected expenses.
Limited Customization: Advanced users may encounter limitations when attempting to customize certain aspects of the SageMaker environment and algorithms.
Data Privacy Concerns: The cloud-based data storage raises concerns for users with strict data locality requirements or those subject to stringent data privacy regulations.
Dependency on AWS: To maximize SageMaker's capabilities, users often need to rely on the broader AWS ecosystem, potentially resulting in vendor lock-in.
Offline Processing Challenges: While designed for real-time inference, SageMaker may not be optimized for batch processing or offline use cases, limiting its versatility.
Resource Constraints: The platform's performance can be constrained by the chosen instance types, affecting the speed of model training and inference.
Complexity for Small Projects: Some users find SageMaker's robust features excessive for small-scale projects, leading to a steeper learning curve without commensurate benefits.
AutoML Limitations: While AutoML is a strength, it may not cover all use cases, and users may need to resort to manual interventions for specific scenarios.
Documentation Gaps: A few users have reported occasional gaps or ambiguities in the platform's documentation, which can be frustrating for troubleshooting and implementation.
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Cost: All users who discuss its pricing say that the cost of acquisition is high.
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User reviews of Amazon SageMaker reveal a platform appreciated for its robust feature set, scalability, and cost-efficiency. Many users find its comprehensive tools for data preprocessing, model training, deployment, and monitoring to be a significant strength. Scalability is another key advantage, with SageMaker accommodating both small-scale experiments and large-scale production workloads effectively. However, some users point out that SageMaker has a steep learning curve, particularly for beginners, and cost management can be challenging, especially during extensive model training. The platform's dependency on the broader AWS ecosystem can lead to vendor lock-in, which may not be ideal for organizations seeking flexibility. SageMaker's AutoML capabilities, such as Autopilot, are praised for automating complex tasks, but some advanced users note limitations in customization. Additionally, while designed for real-time inference, it may not be optimized for batch processing or offline use cases. In comparison to similar products, SageMaker stands out for its deep integration with AWS services, making it a preferred choice for those already within the AWS ecosystem. However, the learning curve and potential cost challenges are factors that users weigh against its benefits. The platform's active community support and extensive documentation receive positive mentions, contributing to a smoother user experience. Overall, Amazon SageMaker is a powerful tool for machine learning but requires careful consideration of its complexities and potential cost implications.

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SAS Viya is an AI-powered data management and visual analytics platform with a robust, scalable architecture. All users who reviewed data source connectivity said that it connects to multiple sources and integrates easily with business applications, giving a seamless user experience. With fast in-memory processing of big data sets, it leverages the power of R to enable visual statistics. All users who mentioned predictive analysis said that it enables automated forecasting through what-if scenarios, goal-seeking, text mining and decision trees. Citing ease of use, all users say that the platform is intuitive and enables easy data modeling and self-service visual analytics. All users who mentioned support said that they are responsive and knowledgeable. Around 71% of the users who comment on its functionality say that it is a robust, scalable and flexible platform that enables visualization and analysis of business data, though some users say visual statistics need improvement. On the flip side, all users who review its cost say that the tool is expensive. In summary, SAS Viya is an analytics tool that provides data management, visualization and AI-powered analytics to enterprises for improved decision making, though small organizations and startups might find it cost-prohibitive.

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