Pricing score

Red Hat Pricing Profile

Red Hat is a flexible, scalable AI and ML platform that enables enterprises to create and deliver AI-enabled applications at scale across hybrid cloud environments.

HQ Location

Raleigh, North Carolina, USA

Category:

Key Takeaways

Collaborative Environment

OpenShift Data Science provides a collaborative environment where data scientists, devs, and IT ops teams can work together on ML models, experiment, and iterate.

Data Preparation

OpenShift Data Science allows you to manage and preprocess large datasets using distributed processing tools like Apache Spark and Dask.

Model Deployment

Seamlessly deploy models into production using Kubeflow, Seldon Core, or other container-based tools, with automated scaling and load balancing.

Product Overview

Red Hat OpenShift Data Science is an enterprise-grade, cloud-native platform for building, training, and deploying machine learning models at scale. It is a part of the Red Hat OpenShift suite of Kubernetes-based solutions, designed to provide a unified environment for data scientists, developers, and IT operations teams to collaborate on AI and machine learning (ML) workflows.

OpenShift Data Science leverages Kubernetes for container orchestration and provides integrated tools for managing the full lifecycle of machine learning models, from data preparation and model training to deployment and monitoring. It also integrates seamlessly with Red Hat OpenShift, providing a secure, scalable, and reliable environment for building AI/ML applications in both on-premises and cloud environments.

INSIGHTS

Our insights about Red Hat pricing

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How much does Red Hat cost?

Red Hat does not publicly disclose its pricing on its website, as costs can vary based on factors like company size, the products selected, and specific requirements. For the most accurate and personalized pricing information, it’s best to contact Red Hat directly through their website or speak with a sales representative.

What users say about Red Hat pricing

kelly R.

My overall experience with Red Hat OpenShift Data Science has been excellent. The software has exceeded my expectations in terms of its performance and ease of use. Additionally, the support and documentation provided by Red Hat has been extremely helpful in resolving any issues or concerns that have arisen. It is especially suitable for research and development projects, as well as for companies that require real-time data analysis. Its ability to process large volumes of data and its integration with other tools allows users to efficiently.

Jaime M.

Hat Red With containerization, OpenShift Data Science offers a distinctive method for managing data science workflows. We may use this capability to package up our financial models, algorithms, and data pipelines, assuring consistency and reproducibility throughout different phases of research. It streamlines the creation and application of sophisticated financial models, improving the effectiveness of our job. Data that is current is essential for financial analysis. We can evaluate and respond to financial data as it is generated or received thanks to OpenShift Data Science's capability for real-time data processing, which distinguishes it from many other platforms. For monitoring market trends, adapting investment plans to shifting economic conditions, and tracking market movements, this real-time capability is crucial.

miguel g.

Excellent platform that combines the flexibility and scalability of Red Hat OpenShift with the capabilities of data science. This solution offers a centralized, integrated environment that makes it easy to develop, deploy, and manage data science applications. The ability to transform large volumes of data into relevant and actionable information has fueled the growth and success of many companies.

Adrian Andres J.

Scalability-enabling containerization may also need a lot of resources. Running numerous containers at once might place a burden on hardware resources and demand a lot of processing power. Hardware changes might be required as a result, which would raise the overall implementation cost.

Marcos P.

When it comes to effortlessly incorporating containerization into the machine learning workflow, Red Hat OpenShift Data Science excels. This functionality makes sure that machine learning models created in one environment can be reliably applied during other production and development stages. It makes the transition from development to production seamless and gets rid of the compatibility problems sometimes connected with model deployment. It offers a central platform where analysts, engineers, and data scientists can easily cooperate. This collaborative setting encourages knowledge exchange, quickens project turnaround times, and improves the caliber of machine learning models.

kelly R.

Adrian Andres J.

Jaime M.

Marcos P.

miguel g.

Red Hat Pricing Rating

AutoML: 4.8/5

OpenShift Data Science can be extended with tools for AutoML capabilities, helping users automatically build and optimize machine learning models. These tools are designed to simplify the process for data scientists and other users who may not have deep expertise in machine learning.

Customization: 4.8/5

OpenShift Data Science supports a wide range of open-source and third-party AI/ML frameworks, including tools for natural language processing (NLP), computer vision, and reinforcement learning.

Scalability: 4.9/5

Run AI and ML workloads at scale, using Kubernetes to efficiently manage resources for large datasets and complex models.

FAQ on Red Hat Pricing

Who is Red Hat OpenShift Data Science for?

OpenShift Data Science is aimed at organizations and teams involved in data science, machine learning, and AI projects. It caters to:

- Data Scientists: For building, training, and experimenting with ML models.
- Developers: For integrating ML models into production applications.
- IT Operations Teams: For managing infrastructure, scaling resources, and ensuring security and compliance. It is ideal for enterprise users who need to scale machine learning workloads while maintaining control over security and governance.

Do I need to be a Kubernetes expert to use OpenShift Data Science?

No, you do not need to be an expert in Kubernetes. While OpenShift Data Science is built on Kubernetes, it abstracts much of the complexity away. The platform provides tools such as Jupyter Notebooks, Kubeflow Pipelines, and MLflow to simplify the ML lifecycle, enabling data scientists and developers to focus on building and deploying models rather than managing infrastructure.

Can I use GPUs for model training in OpenShift Data Science?

Yes, Red Hat OpenShift Data Science supports GPU acceleration for training deep learning models. The platform allows you to use GPUs (NVIDIA) for faster computation when working with resource-intensive models, such as deep neural networks. OpenShift Data Science supports multi-node and multi-GPU configurations for large-scale model training.

How does OpenShift Data Science handle resource scaling?

OpenShift Data Science is built on Kubernetes, which allows for elastic scaling of resources. The platform can automatically scale compute resources (such as CPU, memory, and GPUs) based on workload demand. Kubernetes handles the orchestration of containers, ensuring that resources are allocated efficiently and that workloads can scale up or down as needed.