Developing AI-driven solutions with Azure Red Hat OpenShift
Here in this blog, we will learn how to develop AI-driven solutions with Azure Red Hat Open Shift.
One of the most potent technological advancements in recent decades is generative artificial intelligence or gen AI. As a result, a lot of businesses are rushing to determine how to best apply it while continuing to operate and expand.
The fact that model selection, training, tweaking, and lifespan are only a portion of the picture is among the most crucial factors to take into account while implementing gen AI. A model is frequently provided as a component of a cloud-native application. Delivering a business-ready application—the “last mile”—is essential to generating a return on investment. For you to stay ahead of the competition, your company needs a strong and adaptable base. Additionally, as Red Hat recommended in a recent Forbes piece, be sure to invest in a reliable platform that can handle that degree of innovation.
Azure Red Hat OpenShift can help reduce the time to value AI projects by pre-integrating the DevOps pipeline with data science tasks. Data scientists and data engineers would prefer to manage gen AI models and solve business problems instead of wasting valuable time on infrastructure and DevOps tools.
How does this appear in practice? Red Hat and Microsoft are collaborating on a practical workshop to show how various AI tools and services may be integrated with Azure Red Hat OpenShift and open-source solutions on Microsoft Azure to speed up application delivery.
Azure Red Hat OpenShift, Red Hat OpenShift AI, Azure OpenAI models, open source models, Azure compute and data services, and the Azure AI Studio in order to support retrieval-augmented generation (RAG) functionality for enhancing the performance of gen AI models are all highlighted in the workshop by Red Hat and Microsoft. Businesses have the freedom to select the best MLOps platform for their data science and business teams to quickly prototype and turn into a business application, regardless of whether they are using Azure AI services as the data tool, Red Hat OpenShift AI as their main AI product, or both.
Red Hat and Azure Services-integrated technology in the Parasol Workshop
A quick look at the workshop
Let’s explore what this workshop will teach you in greater detail. We illustrate how historical claims data may be utilized to optimize an AI model for a chatbot application using a fictitious insurance firm. This example may have been shown to you for the first time during the keynote address at the Red Hat Summit 2024. We have added a twist to this demonstration by comparing models from various sources with various scenarios, ranging from emotion analysis to text summarization, utilizing both Red Hat OpenShift AI and Azure Open AI. The result is then improved by supplementing the prompt data with external documentation using an RAG vector database. The program will then illustrate a typical data scientist assignment by combining a YOLO picture classification model with an extra tuning and training step. Finally, we show how to deploy the customized model in a model server so that it can be accessed via an API.
Azure Red Hat OpenShift, OpenShift AI, and Azure OpenAI interaction with Parasol Workshop
The data science and development team can utilize a GitOps deployment template in ArgoCD to launch an integrated application that incorporates the AI models into a business-ready application after the models are tested and prepared for deployment. As new models, requirements, and improvements are needed, this can be iterated by automating the MLOps and DevOps interface. The ROI of building AI applications may be greatly accelerated by combining MLOps with a turnkey DevOps platform that is accessible to both technical and non-technical people.
GitOps distributed the final integrated application with several models.
Get AI solutions available for production more quickly.
When organizing application modernization activities in your AI journey, you have a lot of objectives to meet, needs to take into account, and solutions to select from. It’s crucial to have a dependable platform that gives you the freedom to select the best models and AI tools to help you scale and deliver applications more quickly. The workshop demonstrates how Azure Open AI services and Red Hat OpenShift AI can enable enterprises to develop, implement, and oversee AI solutions at scale. By accelerating AI adoption in commercial settings with Azure Red Hat OpenShift, businesses can concentrate on using AI to create value rather than maintaining complicated infrastructure. These solutions can shorten and simplify the journey from concept to AI solutions that are ready for production.