One of the key challenges in AI projects is bridging the gap between data science and engineering. Engineering and operations teams often lack expertise in the complexities of AI, data science, and machine learning, while data scientists may not have the engineering skills or resources needed to efficiently build, deploy, and monitor models in modern cloud environments.
Organizations starting with machine learning often rely on manual workflows, where data scientists handle most of the workflow steps manually and may struggle with collaboration or resources needed to gather data, train models, or deploy them into production.
Machine learning operations (MLOps) is a set of practices aimed at optimizing the lifecycle of machine learning projects, allowing teams to iterate quickly and efficiently to deliver ML models to production. MLOps streamlines the ML workflow by applying the same principles as DevOps: continuous improvement, maximized automation, and fostering collaboration between data scientists, engineers, and administrators.
Red Hat OpenShift AI (RHOAI) is a platform designed for data scientists, AI professionals, developers, machine learning engineers, and operations teams to prototype, build, deploy, and monitor AI models. With its MLOps approach, RHOAI enables teams to efficiently manage the ML lifecycle and navigate complex machine learning environments. As a modular platform supporting the entire AI/ML project lifecycle in cloud environments, RHOAI is delivered as an OpenShift operator, integrating tools to automate ML workflows, manage data science processes, and streamline model serving and monitoring. It leverages open-source technologies like Project Jupyter and Kubeflow.
Additionally, Red Hat collaborates with leading AI vendors like Anaconda, Intel, IBM, and NVIDIA to integrate their tools into RHOAI. These components can be optionally activated to extend RHOAI’s functionality, with installation options ranging from operators to Red Hat Ecosystem integrations and workbench images.
The following topics will be covered in this webinar:
- Machine Learning Models
- Training Models
- Enhancing Model Training with RHOAI
- Model Serving in RHOAI
- Deploying Machine Learning Model with RHOAI
Any questions? Please email Muhammad Usman.
Weinan Z
Senior Technical Account Manager, Red Hat Certified Architect, Red Hat
Weinan Z has been working with OpenShift application development and infrastructure architecture for the past five years, collaborating closely with customers on OpenShift migrations, upgrades, and architecture design. He serves as a technical advisor for enterprises, helping them effectively utilize OpenShift. Ten years ago, Weinan focused on neural network optimization and model design, particularly in object detection within computer vision, and worked on natural language processing, applying feature extraction techniques. He also spent five years in machine learning research, leading a project to integrate object detection into software companies, helping them achieve automated cross-platform mobile UI testing using neural network algorithms.
