Course description
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.
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.
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.
