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.
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 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
The lack of integration with production platforms and tools can make AI implementation costly and difficult.
Disparate Inference Types
Varied inference types—real-time, batch, audio streaming, and ensemble—require different types of optimizations.