Introduction

Customers are increasingly seeking to adopt generative AI solutions to enhance productivity, drive innovation, and gain a competitive edge. There is a need for rapid development and an easier path from pilot to production. AI workflows are created to provide developers with a packaged set of microservices to experience and experiment with AI solutions with a clear path to production.

NVIDIA AI Workflows provide reference examples of how to leverage NVIDIA NIM inference microservices and frameworks to build AI solutions for solving business problems. These workflows provide guidance for a variety of AI tasks. These include but are not limited to fine-tuning, AI model creation with NVIDIA technology, and putting the same to use in industry vertical or horizontal use cases.. The pipelines to create applications are highlighted, as well as guidance 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 as governed by your security model, following the example provided by the workflow to:

• Reduce development time and cost

• Improve accuracy and performance

• Gain confidence in outcomes, by leveraging NVIDIA AI expertise

The example workflow identifies which NVIDIA NIMs and AI frameworks 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 and can be accessed for free with a 90-day trial license.

NVIDIA AI Workflows contain the AI framework as well as tools for automating a cloud-native solution. AI Workflows also have packaged components that include enterprise-ready implementations with best practices that ensure reliability, security, performance, scalability, and interoperability, while allowing a path for you to deviate.

A typical workflow may look similar to the following diagram:

Within each workflow, opinionated guidance and example components are provided at each of the layers within this stack, along with information about how to integrate the AI solution with these components:

Hardware

NVIDIA AI Enterprise supported GPU-accelerated on-premise hardware or cloud instances are required.

Applications

Example microservices are provided as a series of Helm charts and customized containers that are deployed as a part of the workflow, to demonstrate how to customize and build an AI application using NVIDIA frameworks, and integrate this application with other microservices and enterprise software components.