AssemblyAI

SEPTEMBER 18, 2023

LLMs leverage deep learning architectures to process and understand the nuances and context of human language. It offers a simple API for applying LLMs to up to 100 hours of audio data, even exposing endpoints for common use tasks It's smart enough to auto-generate subtitles, identify speakers, and transcribe audio in real time.

Large Language Models 130

Large Language Models BERT LLM Auto-complete 130

AWS Machine Learning Blog

MARCH 2, 2023

Transformer-based language models such as BERT ( Bidirectional Transformers for Language Understanding ) have the ability to capture words or sentences within a bigger context of data, and allow for the classification of the news sentiment given the current state of the world. The code can be found on the GitHub repo. eks-create.sh

BERT 75

BERT Deep Learning Metadata Auto-complete 75

AWS Machine Learning Blog

MAY 8, 2023

TensorRT is an SDK developed by NVIDIA that provides a high-performance deep learning inference library. It’s optimized for NVIDIA GPUs and provides a way to accelerate deep learning inference in production environments. Triton Inference Server supports ONNX as a model format.

ML 83

ML BERT Deep Learning Auto-complete 83

DataRobot Blog

FEBRUARY 2, 2023

The creation of foundation models is one of the key developments in the field of large language models that is creating a lot of excitement and interest amongst data scientists and machine learning engineers. These models are trained on massive amounts of text data using deep learning algorithms. pip install transformers==4.25.1

BERT 52

BERT Large Language Models Natural Language Processing Machine Learning 52

Google Research AI blog

FEBRUARY 28, 2023

We also help make global conferences accessible to more researchers around the world, for example, by funding 24 students this year to attend Deep Learning Indaba in Tunisia. Dataset Description Auto-Arborist A multiview urban tree classification dataset that consists of ~2.6M

Robotics 72

Robotics Deep Learning Auto-classification ML 72

AWS Machine Learning Blog

APRIL 17, 2023

However, as the size and complexity of the deep learning models that power generative AI continue to grow, deployment can be a challenging task. Then, we highlight how Amazon SageMaker large model inference deep learning containers (LMI DLCs) can help with optimization and deployment.

Prompt Engineer 78

Prompt Engineer Prompt Engineering Deep Learning Large Language Models 78

AWS Machine Learning Blog

MARCH 6, 2023

Large language models (LLMs) are neural network-based language models with hundreds of millions ( BERT ) to over a trillion parameters ( MiCS ), and whose size makes single-GPU training impractical. This results in faster restarts and workload completion. The following GitHub repos offer examples with PyTorch and TensorFlow.

Large Language Models 78

Large Language Models LLM Machine Learning ML 78

Jay Alammar

DECEMBER 16, 2020

This article focuses on auto-regressive models, but these methods are applicable to other architectures and tasks as well. In generating the second token to complete the date, the name still is the most important with 60% importance, followed by the first portion of the date -- a model output, but an input to the second time step.

Explainability 52

Explainability Auto-complete Auto-classification Neural Network 52

AWS Machine Learning Blog

NOVEMBER 1, 2023

Utilizing the latest Hugging Face LLM modules on Amazon SageMaker, AWS customers can now tap into the power of SageMaker deep learning containers (DLCs). Additionally, you benefit from advanced features like auto scaling of inference endpoints, enhanced security, and built-in model monitoring. This model achieves a 91.3%

Auto-classification 92

Auto-classification LLM Auto-complete Generative AI 92

AWS Machine Learning Blog

FEBRUARY 24, 2023

In addition, load testing can help guide the auto scaling strategies using the right metrics rather than iterative trial and error methods. Solution overview For an introduction to MMEs and MMEs with GPU, refer to Create a Multi-Model Endpoint and Run multiple deep learning models on GPU with Amazon SageMaker multi-model endpoints.

BERT 87

BERT NLP Computer Vision Neural Network 87

AWS Machine Learning Blog

JANUARY 9, 2023

The models can be completely heterogenous, with their own independent serving stack. After these business logic steps are complete, the inputs are passed through to ML models. And during idle time, it should be able to turn off compute capacity completely so that you’re not charged. ML inference options.

ML 74

ML Auto-complete Auto-classification Deep Learning 74