Modern enterprise applications are becoming more cloud-native and based on a microservices architecture. Microservices, by definition, consist of a collection of small independent services that communicate over well-defined APIs. AI applications, in most instances, adhere well to this same architectural design, as there are typically many different components that all need to work together in both training and inferencing workflows.
To deploy an application in a production environment, the application must also meet the following criteria:
Reliability
Security
Performance
Scalability
Interoperability
NVIDIA AI Workflows are intended to provide reference solutions of how to leverage NVIDIA frameworks to build AI solutions for solving common use cases. These workflows guide fine-tuning and AI model creation to build upon NVIDIA frameworks. The pipelines to create applications are highlighted, as well as opinions on how to deploy customized applications and integrate them with various components typically found in enterprise environments, such as components for orchestration and management, storage, security, networking, etc.
By leveraging an AI workflow for your specific use case, you can streamline the development of AI solutions following the example provided by the workflow to:
Reduce development time, at a lower cost
Improve accuracy and performance
Gain confidence in the outcome, by leveraging NVIDIA AI expertise
Using the example workflow, you know exactly what AI framework to use, how to bring data into the pipeline, and what to do with the data output. AI Workflows are designed as microservices, which means they can be deployed on Kubernetes alone or with other microservices to create a production-ready application for seamless scaling. The workflow cloud deployable package can be used across different cloud instances and is automatable and interoperable.
NVIDIA AI Workflows are available on NVIDIA NGC for NVIDIA AI Enterprise software customers.