Books, Data Preparation and Natural Language Processing

Books

Data Preparation

Natural Language Processing

5 Top Large Language Models & Generative AI Books

Towards AI

AUGUST 6, 2024

Master LLMs & Generative AI Through These Five Books This article reviews five key books that explore the rapidly evolving fields of large language models (LLMs) and generative AI, providing essential insights into these transformative technologies.

Natural Language Processing

Natural Language Processing AI AI AWS

Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

Flipboard

NOVEMBER 20, 2024

Knowledge base – You need a knowledge base created in Amazon Bedrock with ingested data and metadata. For detailed instructions on setting up a knowledge base, including data preparation, metadata creation, and step-by-step guidance, refer to Amazon Bedrock Knowledge Bases now supports metadata filtering to improve retrieval accuracy.

AWS

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Webinars

Apache Airflow®: The Ultimate Guide to DAG Writing

The 2nd Generation of Innovation Management: A Survival Guide

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Trending Sources

Best practices and lessons for fine-tuning Anthropic’s Claude 3 Haiku on Amazon Bedrock

AWS Machine Learning Blog

NOVEMBER 1, 2024

Fine-tuning is a powerful approach in natural language processing (NLP) and generative AI , allowing businesses to tailor pre-trained large language models (LLMs) for specific tasks. This process involves updating the model’s weights to improve its performance on targeted applications. Sonnet across various tasks.

Data Preparation

Data Preparation Machine Learning Machine Learning ML

Webinars

Apache Airflow®: The Ultimate Guide to DAG Writing

The 2nd Generation of Innovation Management: A Survival Guide

MORE WEBINARS

Build well-architected IDP solutions with a custom lens – Part 2: Security

AWS Machine Learning Blog

NOVEMBER 22, 2023

An intelligent document processing (IDP) project usually combines optical character recognition (OCR) and natural language processing (NLP) to read and understand a document and extract specific entities or phrases. She focuses on NLP-specific workloads, and shares her experience as a conference speaker and a book author.

AWS

AWS ML ML Machine Learning

Your guide to generative AI and ML at AWS re:Invent 2023

AWS Machine Learning Blog

NOVEMBER 22, 2023

You marked your calendars, you booked your hotel, and you even purchased the airfare. In this code talk, learn how to prepare data at scale using built-in data preparation assistance, co-edit the same notebook in real time, and automate conversion of notebook code to production-ready jobs. We’ll see you there!

AWS

AWS ML ML AI

Build production-ready generative AI applications for enterprise search using Haystack pipelines and Amazon SageMaker JumpStart with LLMs

AWS Machine Learning Blog

AUGUST 14, 2023

Haystack FileConverters and PreProcessor allow you to clean and prepare your raw files to be in a shape and format that your natural language processing (NLP) pipeline and language model of choice can deal with. An indexing pipeline may also include a step to create embeddings for your documents.

AWS

AWS AI AI Database

How Fastweb fine-tuned the Mistral model using Amazon SageMaker HyperPod as a first step to build an Italian large language model

AWS Machine Learning Blog

DECEMBER 18, 2024

This strategic decision was driven by several factors: Efficient data preparation Building a high-quality pre-training dataset is a complex task, involving assembling and preprocessing text data from various sources, including web sources and partner companies. The team opted for fine-tuning on AWS.

Clustering

Clustering AWS AI AI

A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

SEPTEMBER 11, 2024

The benchmark used is the RoBERTa-Base, a popular model used in natural language processing (NLP) applications, that uses the transformer architecture. Any of these parsing steps that run in parallel could be sped up relative to sequential processing.

AWS

AWS ML ML Clustering

Advanced RAG patterns on Amazon SageMaker

AWS Machine Learning Blog

MARCH 28, 2024

Data preparation In this post, we use several years of Amazon’s Letters to Shareholders as a text corpus to perform QnA on. For more detailed steps to prepare the data, refer to the GitHub repo. For step-by-step instructions, refer to the GitHub repo. SageMaker JumpStart is at the center of this solution.

AWS

AWS Machine Learning Machine Learning AI

Here’s how Snorkel Flow + Google AI built an enterprise-ready model in a day

Snorkel AI

MARCH 19, 2024

Google’s thought leadership in AI is exemplified by its groundbreaking advancements in native multimodal support (Gemini), natural language processing (BERT, PaLM), computer vision (ImageNet), and deep learning (TensorFlow). Book a demo today. See what Snorkel option is right for you.

AI AI Data Scientist ML

Here’s how Snorkel Flow + Google AI built an enterprise-ready model in a day

Snorkel AI

MARCH 19, 2024

Google’s thought leadership in AI is exemplified by its groundbreaking advancements in native multimodal support (Gemini), natural language processing (BERT, PaLM), computer vision (ImageNet), and deep learning (TensorFlow). See what Snorkel can do to accelerate your data science and machine learning teams.

AI AI Data Scientist ML

Best Data Annotation Tools for Machine Learning That You Need to Know

DagsHub

MAY 27, 2024

Source: Author Introduction Just like having a massive pile of books won't make you a genius unless you read and understand them, a mountain of data won't make a powerful AI if it's not properly labeled. Apart from support for various data types, a tool should also be versatile in terms of support for different annotation types.

Machine Learning

Machine Learning Machine Learning Natural Language Processing AWS

Continual Learning: Methods and Application

The MLOps Blog

FEBRUARY 22, 2024

Chip Huyen, in her excellent book “ Designing Machine Learning Systems ,” distinguishes four stages of advancement: Manual, stateless retraining : There is no automation. through Cron ), and the whole pipeline (data preparation, training) is automated. There is no incremental training and no continual learning.

Machine Learning

Machine Learning Machine Learning ML ML

Accelerate client success management through email classification with Hugging Face on Amazon SageMaker

AWS Machine Learning Blog

SEPTEMBER 12, 2023

By implementing a modern natural language processing (NLP) model, the response process has been shaped much more efficiently, and waiting time for clients has been reduced tremendously. In the following sections, we break down the data preparation, model experimentation, and model deployment steps in more detail.

Data Science

Data Science Data Scientist AWS ML

Techniques for reducing costs in LLM architectures

DagsHub

JULY 15, 2024

Introduction Large Language Models (LLMs) represent the cutting-edge of artificial intelligence, driving advancements in everything from natural language processing to autonomous agentic systems. Advanced mathematical approaches and architectures have significantly enhanced LLMs' capabilities.

Azure

Azure AI AI Database

Fine-tune Meta Llama 3.2 text generation models for generative AI inference using Amazon SageMaker JumpStart

AWS Machine Learning Blog

NOVEMBER 11, 2024

SageMaker Studio is an IDE that offers a web-based visual interface for performing the ML development steps, from data preparation to model building, training, and deployment. This fine-tuning process involves providing the model with a dataset specific to the target domain. Person1#: So, how do you create your heroes?

AI AI ML ML

Data Science Current

5 Top Large Language Models & Generative AI Books

Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

Webinars

Trending Sources

Best practices and lessons for fine-tuning Anthropic’s Claude 3 Haiku on Amazon Bedrock

Webinars

Build well-architected IDP solutions with a custom lens – Part 2: Security

Your guide to generative AI and ML at AWS re:Invent 2023

Build production-ready generative AI applications for enterprise search using Haystack pipelines and Amazon SageMaker JumpStart with LLMs

How Fastweb fine-tuned the Mistral model using Amazon SageMaker HyperPod as a first step to build an Italian large language model

A review of purpose-built accelerators for financial services

Advanced RAG patterns on Amazon SageMaker

Here’s how Snorkel Flow + Google AI built an enterprise-ready model in a day

Here’s how Snorkel Flow + Google AI built an enterprise-ready model in a day

Best Data Annotation Tools for Machine Learning That You Need to Know

Continual Learning: Methods and Application

Accelerate client success management through email classification with Hugging Face on Amazon SageMaker

Techniques for reducing costs in LLM architectures

Fine-tune Meta Llama 3.2 text generation models for generative AI inference using Amazon SageMaker JumpStart

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