How to Deploy, Run, and Scale AI Models with Ease

Read this whitepaper for tips and best practices on standardizing and streamlining your production AI inference.

What Will You Learn?

Advancing your AI project to production can be challenging. Learn how to standardize and streamline your AI inference across multiple model frameworks, different query types, and diverse CPU and GPU infrastructure with NVIDIA Triton™ Inference Server.

Challenges to Deploying AI Models in Production

Multiple Deep Learning and Machine Learning Frameworks

Multiple Frameworks

The existence of multiple deep learning (DL) and machine learning (ML) frameworks, each with their own model execution backend, can be problematic.

Mixed GPU-Based Infrastructure

Mixed Infrastructure

It can be difficult to manage disparate solutions for CPU- and GPU-based infrastructure in the cloud, in the data center, and at the edge.

Scaling Deployment

Scaling Deployment

The lack of integration with production platforms and tools can make AI implementation costly and difficult.

Disparate Inference Types

Disparate Inference Types

Varied inference types—real-time, batch, audio streaming, and ensemble—require different types of optimizations.

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