AWS Machine Learning Blog

JUNE 26, 2024

Technical architecture and key steps The multi-modal agent orchestrates various steps based on natural language prompts from business users to generate insights. For unstructured data, the agent uses AWS Lambda functions with AI services such as Amazon Comprehend for natural language processing (NLP).

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AWS Machine Learning Blog

APRIL 19, 2023

The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. Business requirements We are the US squad of the Sportradar AI department.

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AWS Machine Learning Blog

MAY 31, 2023

Actually, you can develop such a system using state-of-the-art language models and a few lines of Python. Large language models In recent years, language models have seen a huge surge in size and popularity. We use a small Python function to parse the output and simulate an interactive test.

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Towards AI

JULY 21, 2023

Photo by Will Truettner on Unsplash NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER NLP News Cypher | 07.26.20 Instead of building a model from… github.com NERtwork Awesome new shell/python script that graphs a network of co-occurring entities from plain text! Primus The Liber Primus is unsolved to this day.

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AWS Machine Learning Blog

JANUARY 17, 2024

Solution overview In this blog, we will walk through the following scenarios : Deploy Llama 2 on AWS Inferentia instances in both the Amazon SageMaker Studio UI, with a one-click deployment experience, and the SageMaker Python SDK. Fine-tune Llama 2 on Trainium instances in both the SageMaker Studio UI and the SageMaker Python SDK.

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Towards AI

JULY 21, 2023

John on Patmos | Correggio NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER The NLP Cypher | 02.14.21 Their infrastructure is built on top of FastAPI and supports Python, Go and Ruby languages. Last Updated on July 21, 2023 by Editorial Team Author(s): Ricky Costa Originally published on Towards AI.

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ODSC - Open Data Science

MARCH 10, 2025

Tools like Python , R , and SQL were mainstays, with sessions centered around data wrangling, business intelligence, and the growing role of data scientists in decision-making. The Deep Learning Boom (20182019) Between 2018 and 2019, deep learning dominated the conference landscape.

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Flipboard

APRIL 16, 2025

Embeddings enable machine learning (ML) models to effectively process and understand relationships within complex data, leading to improved performance on various tasks like natural language processing and computer vision. Install Python 3.9 Lastly, build a Lambda layer that includes two Python libraries.

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AWS Machine Learning Blog

AUGUST 16, 2023

The images document the land cover, or physical surface features, of ten European countries between June 2017 and May 2018. To perform statistical analyses of the data and load images during DINO training, we process the individual metadata files into a common geopandas Parquet file.

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Mlearning.ai

MARCH 9, 2023

2018; Sitawarin et al., 2018; Papernot et al., 2018) investigated the vulnerability of deep learning models to adversarial attacks in medical image segmentation tasks, and proposed a method to improve their robustness. 2018; Pang et al., For instance, Xu et al. Another study by Jin et al. Makelov, A., Schmidt, L.,

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AWS Machine Learning Blog

AUGUST 7, 2023

As an added inherent challenge, natural language processing (NLP) classifiers are historically known to be very costly to train and require a large set of vocabulary, known as a corpus , to produce accurate predictions. Later versions of the GPT model, namely GPT3 and GPT4, are the engine that powers the ChatGPT application.

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Explosion

MARCH 17, 2019

of the spaCy Natural Language Processing library includes a huge number of features, improvements and bug fixes. spaCy is an open-source library for industrial-strength natural language processing in Python. Version 2.1 Check out the release notes for a full overview. Devlin et al.

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Explosion

DECEMBER 28, 2019

We had already decided at the end of 2018 that we wanted to do this and after seven months of planning and hard work, we couldn’t have been happier with the result. Jul 18: After a brief rest following spaCy IRL, Ines took a minute to appear on the Python Bytes podcast with Michael Kennedy and Brian Okken].

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AWS Machine Learning Blog

JUNE 5, 2023

In this post, we show you how to train the 7-billion-parameter BloomZ model using just a single graphics processing unit (GPU) on Amazon SageMaker , Amazon’s machine learning (ML) platform for preparing, building, training, and deploying high-quality ML models. BloomZ is a general-purpose natural language processing (NLP) model.

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AWS Machine Learning Blog

MAY 22, 2023

Fine-tune FLAN-T5 using a Python notebook Our example notebook shows how to use Jumpstart and SageMaker to programmatically fine-tune and deploy a FLAN T5 XL model. It develops insights by recognizing the entities, key phrases, language, sentiments, and other common elements in a document. It can be run in Studio or locally.

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Explosion

MAY 17, 2023

A lot of people are building truly new things with Large Language Models (LLMs), like wild interactive fiction experiences that weren’t possible before. But if you’re working on the same sort of Natural Language Processing (NLP) problems that businesses have been trying to solve for a long time, what’s the best way to use them?

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Snorkel AI

MAY 25, 2023

A brief history of large language models Large language models grew out of research and experiments with neural networks to allow computers to process natural language. From 2018 to the modern day, NLP researchers have engaged in a steady march toward ever-larger models.

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Snorkel AI

MAY 25, 2023

A brief history of large language models Large language models grew out of research and experiments with neural networks to allow computers to process natural language. From 2018 to the modern day, NLP researchers have engaged in a steady march toward ever-larger models.

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Google Research AI blog

FEBRUARY 28, 2023

For example, supporting equitable student persistence in computing research through our Computer Science Research Mentorship Program , where Googlers have mentored over one thousand students since 2018 — 86% of whom identify as part of a historically marginalized group.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

In terms of resulting speedups, the approximate order is programming hardware, then programming against PBA APIs, then programming in an unmanaged language such as C++, then a managed language such as Python. Analysis of publications containing accelerated compute workloads by Zeta-Alpha shows a breakdown of 91.5%

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Explosion

NOVEMBER 21, 2019

sense2vec reloaded: the updated library sense2vec is a Python package to load and query vectors of words and multi-word phrases based on part-of-speech tags and entity labels. from_disk("/path/to/s2v_reddit_2015_md") nlp.add_pipe(s2v) doc = nlp("A sentence about natural language processing.") assert doc[3:6].text

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Explosion

AUGUST 1, 2019

Transformers and transfer-learning Natural Language Processing (NLP) systems face a problem known as the “knowledge acquisition bottleneck”. 2018) in using the vector for the class token to represent the sentence, and passing this vector forward into a softmax layer in order to perform classification.

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AWS Machine Learning Blog

NOVEMBER 11, 2024

We then also cover how to fine-tune the model using SageMaker Python SDK. FMs through SageMaker JumpStart in the SageMaker Studio UI and the SageMaker Python SDK. Fine-tune using the SageMaker Python SDK You can also fine-tune Meta Llama 3.2 models using the SageMaker Python SDK. You can access the Meta Llama 3.2

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