Who should attend
This experience demonstrates how individuals across different roles must learn to share, collaborate, and work toward a common goal to achieve positive outcomes and drive innovation.
It is especially valuable for:
- MLOps Platform Users: Data scientists, data engineers, and application developers.
- MLOps Platform Providers: Machine learning engineers, MLOps engineers, and platform engineers.
- MLOps Platform Stakeholders: Architects and IT managers.
The scenario incorporates technical aspects of working with machine learning systems, offering practical insights into how these roles can align their efforts.
You will learn how to continuously deliver value to your customers by accelerating the deployment of new models to market. Our instructors will share experiences and best practices learned from engaging directly with customers during Red Hat services engagements.
Prerequisites
- ! or Basic understanding of OpenShift/Kubernetes and containers is helpful
- High level understanding of AI or Red Hat AI Foundations is beneficial
Course Objectives
Impact on the Organization
- Many companies are discovering that their current organizational structure and approaches to machine learning are not equipped to deliver AI-driven transformation outcomes: faster deployment of models, continuous improvement through feedback loops, and solutions that align with user needs. To achieve these goals, companies must adopt and practice MLOps principles and methods, integrating collaboration, automation, and lifecycle management into their AI workflows.
- This course introduces real-world MLOps culture principles and modern practices. You will develop a predictive machine learning model using Red Hat OpenShift and Red Hat OpenShift AI, and other industry-standard MLOps software, tools, and techniques. By the end of the course, you will be equipped to apply MLOps principles and leverage open-source solutions to drive and lead AI transformation initiatives within your organization.
Impact on the Individual
As a result of attending this course, you will experience MLOps culture, explore MLOps practices, and apply your learning to bring a machine learning model into production. After completing the course, you will be able to:
- Apply MLOps principles to streamline the development and deployment of machine learning models.
- Gain hands-on experience with modern tools and processes, covering the entire lifecycle from inner loop development to outer loop operations.
- Enhance your skills in collaborative coding styles with pair and mob programming style.
Course Content
Experience the possibilities of MLOps through proven open culture and practices used by Red Hat to support customer innovation.
- MLOps Practices with Red Hat OpenShift AI (AI500) is a five-day immersive class, offering attendees an opportunity to experience and implement a successful MLOps adoption journey. While many AI or data science training programs focus on a particular framework or technology, this course covers how the best Open Source tools fit together in a full MLOps workflow. It blends continuous discovery, continuous training, and continuous delivery in a highly engaging experience simulating real-world machine learning scenarios.
- To achieve the learning objectives, participants should include multiple roles from across the organization. Data scientists, machine learning engineers, platform engineers, architects, and product owners will gain experience working beyond their traditional silos. The daily routine simulates a real-world delivery team, where cross-functional teams learn how collaboration breeds innovation. Armed with shared experiences and best practices, the team can apply what it has learned to help the organization's culture and mission succeed in the pursuit of new projects and improved processes.
- This course is based on Red Hat OpenShift AI, Red Hat OpenShift GitOps and Predictive AI
Course Content Summary
- This course takes you an end to end journey of a Predictive Intelligent Application use case, from ideation to inner loop experimentation to production, while bringing different personas together to seamlessly collaborate on a single platform.
- This course blends cultural and technical practices into a unique, highly-engaging experience, packed with real-world applications. You will learn MLOps practices and how they build upon each other to improve team alignment and delivery efficiency.
- Most AI training focuses on a particular framework or technology, this course combines the best Open Source tools while giving you the experience of how they fit together to reliably and efficiently build, deploy and maintain AI models in production.