openshift ai on Kubernetes for Scalable AI

OpenShift AI on Kubernetes for Scalable AI is a game-changer.

By integrating AI workloads with Kubernetes, you can automate the deployment and scaling of your AI models, making it easier to get started with AI development.

This combination allows for faster time-to-market and reduced costs, as you can scale your AI infrastructure up or down as needed.

With OpenShift AI, you can also take advantage of automatic resource allocation and load balancing, ensuring that your AI workloads are running smoothly and efficiently.

Red Hat OpenShift AI allows you to rapidly develop, train, serve, and monitor machine learning models on-site, in the public cloud, or at the edge.

This platform provides the necessary tools to streamline the entire machine learning process, making it more efficient and cost-effective.

Red Hat OpenShift AI is specifically designed for on-site, public cloud, or edge deployment, giving you flexibility in where you choose to run your machine learning models.

Building and deploying AI models on OpenShift is a game-changer for data scientists and developers.

OpenShift AI provides a powerful AI/ML platform that allows collaboration between data scientists and developers, enabling them to move from experiment to production quickly.

With OpenShift AI, you can integrate familiar tools and libraries like Jupyter, TensorFlow, and PyTorch into a flexible UI, making it easier to build AI-enabled applications.

Enabling rapid experimentation and model development on OpenShift allows developers to integrate models into their workflows with fewer obstacles.

Operationalizing AI/ML models is made easier with tools like model serving, data science pipelines, and model monitoring, which help data scientists use similar DevOps principles as application developers on the same OpenShift platform.

By leveraging Red Hat's expertise in application platforms powered by Kubernetes, you can move your experimental models to production faster, with fewer obstacles.

Red Hat OpenShift AI is built on open source principles, drawing from the same communities that shape Red Hat's products. This open source foundation ensures that the technology is community-driven and adaptable to evolving needs.

Red Hat's product development cycle has always been rooted in open source, with projects like Fedora serving as the upstream version for Red Hat Enterprise Linux. This approach allows for collaboration and innovation at a rapid pace.

You can also tap into a broad range of validated partner data science and ML tools, including software and SaaS-based offerings from Starburst, Anaconda, IBM Watson, Intel, and Pachyderm, which are integrated directly into the UI.

Red Hat OpenShift AI provides a scalable, flexible foundation to extend the reach of NIM microservices.

This means developers can tap into the power of NVIDIA NIM and create GenAI applications on a familiar, trusted Machine Learning Operations (MLOps) platform.

The integration of NVIDIA NIM with Red Hat OpenShift AI allows for the deployment of NVIDIA NIM in a shared workflow with other AI deployments, bringing increased uniformity and easier management.

NVIDIA NIM offers seamless, scalable AI inferencing on-premises or in the cloud through industry-standard application programming interfaces (APIs).

By backing a broad range of AI models, including open-source community models and NVIDIA AI Foundation models, NIM speeds up GenAI deployment in enterprises.

This collaboration will help developers rapidly build and scale modern enterprise applications using a performance-optimized foundation and embedding models across any cloud or data centre.

The integration of NVIDIA NIM in Red Hat OpenShift AI will also provide integrated scaling and monitoring for NVIDIA NIM deployments in collaboration with other AI model deployments across hybrid cloud environments.

This will give enterprises the means to increase their productivity with GenAI capabilities, such as expanded customer service with virtual assistants, summarisation of IT tickets, and acceleration of business operations with sector-specific satellites.

Red Hat's product development cycle is rooted in open source and community involvement. This approach allows Red Hat's products to be steered by the communities that help develop them.

The upstream project for Red Hat Enterprise Linux is Fedora, which shows the importance of open source in Red Hat's product development.

Choosing the right partners can be a daunting task, but with OpenShift, you have a broad range of options to choose from.

You can select from a variety of validated partner data science and ML tools, such as Starburst, Anaconda, IBM Watson, Intel, and Pachyderm, which are integrated directly into the UI.

These partner offerings are designed to work seamlessly with OpenShift, making it easier to get started and achieve your goals.

Dozens of other partner offerings are available on OpenShift, giving you even more flexibility and options to choose from.

By partnering with these companies, you can leverage their expertise and resources to take your projects to the next level.

Kubernetes is the backbone of Red Hat OpenShift AI, allowing it to scale and manage complex AI workloads.

Red Hat OpenShift AI integrates Kubeflow, an open source framework that simplifies AI/ML workflow deployment at scale.

Kubeflow includes a notebook controller, model serving, and data science pipeline components that are now part of the core product.

Generative AI is being adopted by many organizations to automate tasks and improve efficiency.

According to a recent survey, 70% of businesses are already using or planning to use generative AI in the next two years. This technology has the potential to revolutionize the way we work.

One of the key benefits of generative AI is its ability to automate repetitive tasks, freeing up human workers to focus on more creative and high-value tasks.

For example, OpenShift AI can use generative AI to automate the process of deploying and managing AI models, reducing the time and effort required to get models into production.

By adopting generative AI, businesses can improve their productivity and competitiveness, while also reducing costs and improving customer satisfaction.

The adoption of generative AI is also being driven by the increasing availability of data and computing power, making it easier for organizations to train and deploy AI models.