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

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

By the end of this course, you will understand Kubeflow’s architecture and key components and know how to prepare data, model training, serving, and management within Kubeflow.

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Introduction to AI/ML Toolkits with Kubeflow (LFS147)

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.

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