For North America and regions outside EMEA, Red Hat OpenShift AI provides a cloud service that gives data scientists and developers a powerful AI/ML platform for building intelligent applications. It provides a fully supported environment to rapidly develop, train, and test machine learning (ML) models in the public cloud before deploying in production.
Overview
For North America and regions outside EMEA, Red Hat OpenShift AI enables companies to solve critical business challenges by providing a fully managed cloud service environment on Red Hat OpenShift Service on AWS. Red Hat OpenShift AI allows organizations to quickly build and deploy artificial intelligence (AI)/ML models by integrating open-source applications with commercial partner technology.
Red Hat OpenShift AI is an easy-to-configure cloud service that provides a powerful platform for building AI/ML models and applications. It combines the self-service data scientists and developers want with the confidence enterprise IT demands into one common platform. Common tooling, such as Jupyter notebooks and associated TensorFlow and Pytorch frameworks, are an add-on to Red Hat OpenShift Service on AWS, an application platform cloud service powered by Kubernetes and co-managed by Red Hat and Amazon.
The ML models built in Red Hat OpenShift AI are easily portable to other platforms, allowing teams to deploy them in production, on containers, and in the hybrid cloud. Having a trusted foundation reduces friction throughout the life cycle: From a powerful platform to experiment in, to a broad ecosystem of certified tools, to familiar workflows for collaboration. Red Hat OpenShift AI helps teams get intelligent apps into the market faster-where they can deliver the greatest value to the business.
Highlights
- Through this AI/ML add-on service to Red Hat OpenShift Service for AWS, your data science teams can start their projects faster. Instead of standing up and managing your own Kubernetes infrastructure, you can focus on deploying intelligent applications, integrating the models you develop. Gain added benefits like security and operator life cycle integration, built into intelligent applications to simplify the deployment and maintenance of your models.
- Get quick updates and support for core open source tooling. Red Hat tracks, integrates, tests, and supports common AI/ML tooling like Jupyter, TensorFlow, PyTorch, common libraries like Pandas and NumPy, and the source-to-image framework on our Red Hat OpenShift cloud service, so you and your data scientists do not have to.
- Extend the core Red Hat OpenShift AI platform with other integrated Red Hat services like Red Hat OpenShift Streams for Apache Kafka and several leading AI/ML software technology partners including Starburst, Anaconda, IBM, Intel, and Pachyderm.
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Dimension | Cost/unit |
|---|---|
vCPU usage in hours. Example: 2 hours on m5.xlarge = 8 vCPU hours | $0.022 |
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Customer reviews
kelly R.
Allows you to explore and discover valuable insights
Reviewed on Jan 04, 2024
Review provided by G2
What do you like best about the product?
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.
What do you dislike about the product?
I can only say from my experience that some advanced features may require more specialized technical knowledge, which may limit their use for those who are less familiar with data analysis.
What problems is the product solving and how is that benefiting you?
It has allowed me to perform complex data analysis efficiently and obtain valuable insights for my organization. This software allows us to access advanced tools and functions to process large amounts of data and extract valuable insights. Its use case ranges from data analysis and visualization to the creation of predictive models and the implementation of real-time solutions.
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Adrian Andres J.
Transforming Business Analysis: Containerization for Agile Collaboration
Reviewed on Sep 21, 2023
Review provided by G2
What do you like best about the product?
Containerization offers unrivaled scalability and flexibility in the area of finance, where working with large datasets and complicated algorithms is standard. It enables us to containerize our data science workloads, ensuring reliable performance in a range of settings. This feature greatly speeds up the creation and deployment of financial models. Our financial analysis team benefits greatly from the collaboration that Red Hat OpenShift Data Science fosters. We can work on projects at the same time, keep track of changes, and smoothly combine contributions thanks to its interaction with Git and other version control systems. When working with several stakeholders that need to analyze and contribute to financial models and studies, this skill is important.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
My responsibilities include managing crucial financial analysis, risk evaluations, and modeling. We have changed our strategy with the help of Red Hat OpenShift Data Science. Finance is based on collaboration, which Red Hat OpenShift Data Science excels at fostering. Our financial assessments now have better quality because of version control, collaboration on projects, and traceability of changes.
Now, our team can work together to develop intricate models while utilizing the unique skills of each team member. We got answers more quickly, which allowed us to decide on our investment portfolio in real time. Now that we have complete transparency into the contributions and modifications made by each team member, we can work together to construct complex financial models. This has increased the precision of our models while also speeding up project completion.
Now, our team can work together to develop intricate models while utilizing the unique skills of each team member. We got answers more quickly, which allowed us to decide on our investment portfolio in real time. Now that we have complete transparency into the contributions and modifications made by each team member, we can work together to construct complex financial models. This has increased the precision of our models while also speeding up project completion.
Jaime M.
Real-Time Data Processing and Collaboration: The Key to Business Success with OpenShift Data Science
Reviewed on Sep 18, 2023
Review provided by G2
What do you like best about the product?
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.
What do you dislike about the product?
The platform can become quite demanding when dealing with large amounts of data. A robust hardware infrastructure is necessary to take full advantage of its capabilities.
What problems is the product solving and how is that benefiting you?
By enabling us to containerize complex models, OpenShift Data Science and Machine Learnig platform has substantially enhanced my job and sped up our financial modeling and forecasting procedures. The transition from development to production is made easier and results are guaranteed to be consistent. Our approach to handling financial data has changed as a result of its containerization, real-time data processing, and collaborative capabilities. I and other finance professionals can make quick, accurate judgments based on data thanks to this platform. Financial success depends on staying ahead of market trends and economic upheavals. We have the ability to quickly make educated decisions thanks to real-time data processing capabilities. As a result, we are better able to predict the financial future, which helps us plan out our resource allocation and investment strategies more effectively.
Marcos P.
The powerfulness of model deployment
Reviewed on Sep 01, 2023
Review provided by G2
What do you like best about the product?
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.
What do you dislike about the product?
Red Hat OpenShift Data Science shines as a reliable platform in the field of machine learning. It has excellent orchestration of ML pipelines. Nonetheless, there is still potential for improvement in terms of streamlining the deployment procedure and providing a more seamless conversion from model development to practical use.
What problems is the product solving and how is that benefiting you?
For predictive maintenance, we had to implement a sophisticated machine learning model. The model performed consistently in our production environment thanks to the containerization characteristics of Red Hat OpenShift Data Science. This not only helped us save time, but it also increased the model's dependability, enabling us to take preventative maintenance measures to minimize downtime.
miguel g.
Innovative and powerful solution for advanced analytics.
Reviewed on Aug 29, 2023
Review provided by G2
What do you like best about the product?
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.
What do you dislike about the product?
There is nothing that I dislike about this platform since it allows data scientists to work with the best tools that fit each need and the best preferences in the best way.
What problems is the product solving and how is that benefiting you?
This platform makes it easy to integrate with popular tools and languages like Jupyter Notebooks, Python, and R. This best enables data scientists to work with the tools that best fit their needs and preferences, allowing for easy scalability and flexibility of data science environments. This ensures that applications can grow with the changing needs of the organization.