Introduction to AI/ML Toolkits with Kubeflow (LFS147) Course Overview

Target Audience for Introduction to AI/ML Toolkits with Kubeflow (LFS147)

Introduction: The course "Introduction to AI/ML Toolkits with Kubeflow (LFS147)" equips learners with essential skills to deploy machine learning models using Kubernetes, ideal for aspiring and current data professionals.

Target Audience:

• Data Scientists
• Machine Learning Engineers
• DevOps Engineers
• Data Analysts
• Software Developers
• IT Professionals
• Cloud Engineers
• Technical Project Managers
• AI/ML Researchers
• Students in Computer Science or related fields
• Business Analysts looking to leverage AI/ML
• System Administrators with cloud experience
• Anyone transitioning to AI/ML roles

Learning Objectives - What you will Learn in this Introduction to AI/ML Toolkits with Kubeflow (LFS147)?

Introduction to the Course

The Introduction to AI/ML Toolkits with Kubeflow (LFS147) course equips students with essential skills to leverage Kubeflow for building and deploying machine learning workflows, ensuring a solid foundation in AI/ML practices.

Learning Objectives and Outcomes

• Understand the fundamentals of AI and machine learning concepts.
• Explore the architecture and components of Kubeflow.
• Learn how to set up and configure a Kubeflow environment.
• Design and implement machine learning workflows using Kubeflow Pipelines.
• Gain hands-on experience with model training and hyperparameter tuning.
• Implement data management and pre-processing techniques within Kubeflow.
• Understand the deployment of machine learning models at scale.
• Learn how to monitor and maintain ML models in production.
• Explore integration of Kubeflow with other cloud-native tools and services.
• Develop best practices for collaboration and version control in machine learning projects.