Top 10 Red Hat OpenShift Data Science Alternatives & Competitors

Top 10 Alternatives to Red Hat OpenShift Data Science Recently Reviewed By G2 Community

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Databricks Data Intelligence Platform
By Databricks Inc.
4.6/5
(658)
Product Description
Making big data simple
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Reviewers say compared to Red Hat OpenShift Data Science, Databricks Data Intelligence Platform is:
More usable
Better at meeting requirements
Better at support
Categories in common with Red Hat OpenShift Data Science:
Data Science and Machine Learning Platforms

IBM watsonx.ai
By IBM
4.4/5
(135)
Product Description
IBM Watsonx.ai is an advanced AI and machine learning platform designed to accelerate enterprise AI adoption, offering a comprehensive suite of tools for businesses to build, deploy, and scale AI applications. The product is part of IBM's broader Watsonx ecosystem, which aims to democratize AI by providing accessible, powerful solutions tailored for organizations of all sizes and industries.
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Reviewers say compared to Red Hat OpenShift Data Science, IBM watsonx.ai is:
More usable
Better at support
Categories in common with Red Hat OpenShift Data Science:
Data Science and Machine Learning Platforms

Amazon SageMaker
By Amazon Web Services (AWS)
4.2/5
(48)
Product Description
Amazon SageMaker is a fully managed service that enables data scientists and developers to build, train, and deploy machine learning (ML) models at scale. It provides a comprehensive suite of tools and infrastructure, streamlining the entire ML workflow from data preparation to model deployment. With SageMaker, users can quickly connect to training data, select and optimize algorithms, and deploy models in a secure and scalable environment. Key Features and Functionality: - Integrated Development Environments (IDEs): SageMaker offers a unified, web-based interface with built-in IDEs, including JupyterLab and RStudio, facilitating seamless development and collaboration. - Pre-built Algorithms and Frameworks: It includes a selection of optimized ML algorithms and supports popular frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing flexibility in model development. - Automated Model Tuning: SageMaker can automatically tune models to achieve optimal accuracy, reducing the time and effort required for manual adjustments. - Scalable Training and Deployment: The service manages the underlying infrastructure, enabling efficient training of models on large datasets and deploying them across auto-scaling clusters for high availability. - MLOps and Governance: SageMaker provides tools for monitoring, debugging, and managing ML models, ensuring robust operations and compliance with enterprise security standards. Primary Value and Problem Solved: Amazon SageMaker addresses the complexity and resource-intensive nature of developing and deploying ML models. By offering a fully managed environment with integrated tools and scalable infrastructure, it accelerates the ML lifecycle, reduces operational overhead, and enables organizations to derive insights and value from their data more efficiently. This empowers businesses to innovate rapidly and implement AI solutions without the need for extensive in-house expertise or infrastructure management.
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Reviewers say compared to Red Hat OpenShift Data Science, Amazon SageMaker is:
Better at support
Categories in common with Red Hat OpenShift Data Science:
Data Science and Machine Learning Platforms
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Alteryx
By Alteryx
4.6/5
(671)
Product Description
Alteryx drives transformational business outcomes through unified analytics, data science, and process automation.
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Reviewers say compared to Red Hat OpenShift Data Science, Alteryx is:
More usable
Categories in common with Red Hat OpenShift Data Science:
Data Science and Machine Learning Platforms

MATLAB
By MathWorks
4.5/5
(766)
Product Description
MATLAB is a high-level programming and numeric computing environment widely utilized by engineers and scientists for data analysis, algorithm development, and system modeling. It offers a desktop environment optimized for iterative analysis and design processes, coupled with a programming language that directly expresses matrix and array mathematics. The Live Editor feature enables users to create scripts that integrate code, output, and formatted text within an executable notebook. Key Features and Functionality: - Data Analysis: Tools for exploring, modeling, and analyzing data. - Graphics: Functions for visualizing and exploring data through various plots and charts. - Programming: Capabilities to create scripts, functions, and classes for customized workflows. - App Building: Facilities to develop desktop and web applications. - External Language Interfaces: Integration with languages such as Python, C/C++, Fortran, and Java. - Hardware Connectivity: Support for connecting MATLAB to various hardware platforms. - Parallel Computing: Ability to perform large-scale computations and parallelize simulations using multicore desktops, GPUs, clusters, and cloud resources. - Deployment: Options to share MATLAB programs and deploy them to enterprise applications, embedded devices, and cloud environments. Primary Value and User Solutions: MATLAB streamlines complex mathematical computations and data analysis tasks, enabling users to develop algorithms and models efficiently. Its comprehensive toolboxes and interactive apps facilitate rapid prototyping and iterative design, reducing development time. The platform's scalability allows for seamless transition from research to production, supporting deployment on various systems without extensive code modifications. By integrating with multiple programming languages and hardware platforms, MATLAB provides a versatile environment that addresses the diverse needs of engineers and scientists across industries.
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Reviewers say compared to Red Hat OpenShift Data Science, MATLAB is:
Better at meeting requirements
Better at support
Categories in common with Red Hat OpenShift Data Science:
Data Science and Machine Learning Platforms

Domo
By Domo
4.3/5
(985)
Product Description
Domo is a cloud-native data experience platform that empowers organizations to connect, visualize, and analyze data from diverse sources in real-time. Designed for both technical and non-technical users, Domo facilitates data-driven decision-making across all levels of an organization by providing intuitive dashboards, AI-powered insights, and customizable visualizations. Its scalable architecture ensures seamless integration with existing cloud and on-premise systems, enabling businesses to optimize processes and drive actionable outcomes efficiently. Key Features and Functionality: - Data Integration: Connects with over 1,000 pre-built connectors, allowing seamless access to various data systems, including CRM, ERP, and cloud databases. - Business Intelligence & Analytics: Offers intuitive tools for creating dynamic dashboards, reports, and visualizations, making complex analytics accessible to all teams. - AI and Predictive Insights: Leverages machine learning capabilities to provide predictive analytics and actionable recommendations based on real-time data. - App Creation: Enables the development of custom, low-code, and pro-code business applications that automate processes and streamline decision-making. - Security & Governance: Provides robust data governance tools, including personalized data permissions, custom user roles, and compliance with standards like GDPR and HIPAA. Primary Value and Solutions: Domo addresses the challenge of fragmented data by unifying information from multiple sources into a single platform, enabling organizations to gain comprehensive insights and make informed decisions swiftly. Its user-friendly interface democratizes data access, allowing teams to collaborate effectively using real-time information. By integrating AI-powered analytics and customizable applications, Domo helps businesses optimize operations, identify trends, and drive growth, all while maintaining stringent security and governance standards.
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Categories in common with Red Hat OpenShift Data Science:
Data Science and Machine Learning Platforms

SAS Viya
By SAS Institute Inc.
4.3/5
(729)
Product Description
As a cloud-native AI, analytics and data management platform, SAS Viya enables you to scale cost-effectively, increase productivity and innovate faster, backed by trust and transparency. SAS Viya makes it possible to integrate teams and technology enabling all users to work together successfully to turn critical questions into accurate decisions.
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Categories in common with Red Hat OpenShift Data Science:
Data Science and Machine Learning Platforms

Snowflake
By Snowflake, Inc.
4.6/5
(678)
Product Description
Snowflake’s platform eliminates data silos and simplifies architectures, so organizations can get more value from their data. The platform is designed as a single, unified product with automations that reduce complexity and help ensure everything “just works”. To support a wide range of workloads, it’s optimized for performance at scale no matter whether someone’s working with SQL, Python, or other languages. And it’s globally connected so organizations can securely access the most relevant content across clouds and regions, with one consistent experience.
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Reviewers say compared to Red Hat OpenShift Data Science, Snowflake is:
More usable
Better at meeting requirements
Better at support
Categories in common with Red Hat OpenShift Data Science:
Data Science and Machine Learning Platforms

SAP HANA Cloud
By SAP
4.3/5
(581)
Product Description
SAP HANA Cloud is the cloud-native data foundation of SAP Business Technology Platform, it stores, processes and analyzes data in real time at petabyte scale and converges multiple data types in a single system while managing it more efficiently with integrated multitier storage.
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Categories in common with Red Hat OpenShift Data Science:
Data Science and Machine Learning Platforms

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