AWS Machine Learning Blog

APRIL 20, 2023

Each machine learning (ML) system has a unique service level agreement (SLA) requirement with respect to latency, throughput, and cost metrics. With advancements in hardware design, a wide range of CPU- and GPU-based infrastructures are available to help you speed up inference performance.

ML 103

ML Auto-classification Python Auto-complete 103

AWS Machine Learning Blog

MAY 8, 2023

SageMaker provides single model endpoints (SMEs), which allow you to deploy a single ML model, or multi-model endpoints (MMEs), which allow you to specify multiple models to host behind a logical endpoint for higher resource utilization.

ML 104

ML BERT Deep Learning Auto-complete 104

Marktechpost

OCTOBER 25, 2024

On the other hand, models relying on slow and complete reasoning traces, such as Searchformer, provide better accuracy but underperform due to longer steps of reasoning and its high computational cost. Besides that, when in auto mode, the model selects its strategy, it still stays high, with a high optimal rate of 96.6%

Auto-complete 114

Auto-complete Inference Engine AI AI 114

Mlearning.ai

JULY 9, 2023

Creating a new Space on HuggingFace A “Space” on HuggingFace is a hosting environment that can be used to host your ML app. The following libraries are included in the file: requirements.txt datasets transformers accelerate einops safetensors The complete example can be viewed at: Falcon 7B HuggingFace Spaces.

Large Language Models 52

Large Language Models LLM Python Auto-complete 52

AWS Machine Learning Blog

AUGUST 2, 2023

It also provides a built-in queuing mechanism for queuing up requests, and a task completion notification mechanism via Amazon SNS, in addition to other native features of SageMaker hosting such as auto scaling. To host the asynchronous endpoint, we must complete several steps. amazonaws.com/djl-inference:0.21.0-deepspeed0.8.3-cu117"

Generative AI 89

Generative AI Computer Vision Auto-complete Inference Engine 89