Creating Custom AI Models Using NVIDIA TAO Toolkit with Azure Machine Learning

A fundamental shift is currently taking place in how AI applications are built and deployed. AI applications are becoming more sophisticated and applied to broader use cases. This requires end-to-end AI lifecycle management--from data preparation, to model development and training, to deployment and management of AI apps. This approach can lower upfront costs, improve scalability, and decrease risk for customers using AI applications. While the cloud-native approach to app development can be appealing to developers, machine learning (ML) projects are notoriously time-intensive and cost-intensive, as they require a team with a varied skill set to build and maintain.