AWS Machine Learning Blog

APRIL 20, 2023

We train an XGBoost model for a classification task on a credit card fraud dataset. Model Framework XGBoost Model Size 10 MB End-to-End Latency 100 milliseconds Invocations per Second 500 (30,000 per minute) ML Task Binary Classification Input Payload 10 KB We use a synthetically created credit card fraud dataset.

ML 91

ML Auto-classification Python Auto-complete 91

The MLOps Blog

JUNE 27, 2023

For example, if your team is proficient in Python and R, you may want an MLOps tool that supports open data formats like Parquet, JSON, CSV, etc., The platform also offers features for hyperparameter optimization, automating model training workflows, model management, prompt engineering, and no-code ML app development.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Heartbeat

JULY 17, 2023

Prerequisites To follow along with this tutorial, make sure you: Use a Google Colab Notebook to follow along Install these Python packages using pip: CometML , PyTorch, TorchVision, Torchmetrics and Numpy, Kaggle %pip install - upgrade comet_ml>=3.10.0 !pip Import the following packages in your notebook.

Computer Vision 52

Computer Vision Auto-classification Deep Learning Machine Learning 52

AWS Machine Learning Blog

MARCH 1, 2023

There are several ways to enhance fine tuning through effective prompt engineering and here are a few examples. Sample text prompts to descibe some of the most common design elements of casual long skirts for ladies: Design Style: A-line, wrap, maxi, mini, and pleated skirts are some of the most popular styles for casual wear.

Generative AI 95

Generative AI AI AI Auto-classification 95

Mlearning.ai

JULY 3, 2023

This can be performed using an auto-encoder for instance (remember than an auto-encoder is used to learn efficient low dimensional embeddings of some high dimensional space). Denoising Process Summary Text from a prompt is tokenized and encoded numerically. Scheduler  — essentially ODE integration techniques. pipe = pipe.to(device_name)

Generative AI 52

Generative AI Auto-complete Auto-classification AI 52

AWS Machine Learning Blog

NOVEMBER 1, 2023

The system is further refined with DistilBERT , optimizing our dialogue-guided multi-class classification process. Additionally, you benefit from advanced features like auto scaling of inference endpoints, enhanced security, and built-in model monitoring. TGI is implemented in Python and uses the PyTorch framework.

Auto-classification 112

Auto-classification LLM Auto-complete Generative AI 112

Unite.AI

MARCH 28, 2024

The Inference Challenge with Large Language Models Before the advent of LLMs, natural language processing relied on smaller models focused on specific tasks like text classification, named entity recognition, and sentiment analysis. Let's start by understanding why LLM inference is so challenging compared to traditional NLP models.

Large Language Models 130

Large Language Models LLM Natural Language Processing Prompt Engineer 130