Learning with AI Hub
The collection of assets on AI Hub includes resources that you can use to develop your understanding of machine learning (ML) concepts, technologies, and infrastructure. If you are new to artificial intelligence (AI) and ML, the Machine Learning Crash Course provides the necessary foundation to get started using the ML assets on AI Hub.
Here is an overview of some of the ways you can learn from the assets on AI Hub:
Kubeflow pipelines: Kubeflow pipelines are end-to-end machine learning (ML) workflows based on containers. You can learn best practices for building complex ML workflows by studying the implementation of the reference pipelines on AI Hub.
Notebooks: Jupyter notebooks provide an interactive coding and learning environment. With the Jupyter notebooks on AI Hub, you can get started with ML by studying labs that illustrate ML concepts, or you can learn about complex topics like reinforcement learning and generative adversarial networks.
Services: The collection of services on AI Hub includes APIs, building blocks, ML services, and infrastructure for training and prediction. By investigating the services on AI Hub, you can learn about APIs, ML services, and infrastructure that you can use in you ML system.
Technical guides: The technical guides on AI Hub demonstrate how to implement complex AI use cases. These guides combine services, infrastructure, and custom code to implement complete solutions. By following the technical guides on AI Hub, you learn best practices that you can leverage when designing an AI system.
VM images: The virtual machine (VM) images on AI Hub are designed for ML. You can use these assets to learn about options for designing the infrastructure for your ML system.
By learning from these resources you can apply new technologies and techniques to the design and implementation of your ML service.
- Learn about using AI Hub assets to build your ML service.
- Understand important concepts and terms by reading the introduction to AI Hub.