AWS Machine Learning Blog

AUGUST 22, 2024

It automatically keeps track of model artifacts, hyperparameters, and metadata, helping you to reproduce and audit model versions. The solution has three main steps: Write Python code to preprocess, train, and test an LLM in Amazon Bedrock. This streamlines the process and provides consistency across different stages of the pipeline.

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AWS Machine Learning Blog

AUGUST 14, 2024

This enables the Amazon Q large language model (LLM) to provide accurate, well-written answers by drawing from the consolidated data and information. Additionally, they want access to metadata, timestamps, and access control lists (ACLs) for the indexed documents. The following diagram shows a flowchart of a sync run job.

Metadata 120

Metadata Machine Learning Large Language Models Software Development 120

AWS Machine Learning Blog

JUNE 21, 2024

The LLM models augment SOC investigations with knowledge from eSentire’s security experts and security data, enabling higher-quality investigation outcomes while also reducing time to investigate. Therefore, eSentire decided to build their own LLM using Llama 1 and Llama 2 foundational models.

Generative AI 123

Generative AI LLM AI AI 123

deepsense.ai

JULY 30, 2023

This approach is as important for fine-tuning Large Language Models internally as for projects where you only use LLM-as-a-service for the inference part (think of, e.g., using OpenAI API for calling GPT models). During training, we log all the model metrics and metadata automatically. Why are these elements so important?

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AWS Machine Learning Blog

MAY 21, 2024

The core work of developing a news story revolves around researching, writing, and editing the article. However, when the article is complete, supporting information and metadata must be defined, such as an article summary, categories, tags, and related articles. Storm CMS also gives journalists suggestions for article metadata.

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AWS Machine Learning Blog

JULY 29, 2024

Next, you need to index this data to make it available for a Retrieval Augmented Generation (RAG) approach, where relevant passages are delivered with high accuracy to a large language model (LLM). Amazon Q supports the crawling and indexing of these custom objects and custom metadata.

Metadata 124

Metadata Generative AI ML Software Development 124

AWS Machine Learning Blog

SEPTEMBER 12, 2024

It’s developed by BAAI and is designed to enhance retrieval capabilities within large language models (LLMs). The model supports three retrieval methods: Dense retrieval (BGE-M3) Lexical retrieval (LLM Embedder) Multi-vector retrieval (BGE Embedding Reranker). The LLM processes the request and generates an appropriate response.

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LLM Generative AI Metadata Python 132

AWS Machine Learning Blog

DECEMBER 24, 2024

Training large language models (LLMs) models has become a significant expense for businesses. For many use cases, companies are looking to use LLM foundation models (FM) with their domain-specific data. However, companies are discovering that performing full fine tuning for these models with their data isnt cost effective.

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Deep Learning Generative AI Python Machine Learning 103

Flipboard

DECEMBER 6, 2023

The following are some of the experiments that were conducted by the team, along with the challenges identified and lessons learned: Pre-training – Q4 understood the complexity and challenges that come with pre-training an LLM using its own dataset. In addition to the effort involved, it would be cost prohibitive.

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Chatbots LLM Prompt Engineering Prompt Engineer 167

AWS Machine Learning Blog

JULY 24, 2024

Next you need to index the data to make it available for a Retrieval Augmented Generation (RAG) approach where relevant passages are delivered with high accuracy to a large language model (LLM). In this example, metadata files including the ACLs are in a folder named Meta. For Frequency under Sync run schedule , choose Run on demand.

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AWS Machine Learning Blog

NOVEMBER 7, 2024

Prompt chaining – Generative AI developers often use prompt chaining techniques to break complex tasks into subtasks before sending them to an LLM. A centralized service that exposes APIs for common prompt-chaining architectures to your tenants can accelerate development.

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Generative AI Machine Learning AI AI 112

AWS Machine Learning Blog

APRIL 18, 2024

Solution overview The LMA sample solution captures speaker audio and metadata from your browser-based meeting app (as of this writing, Zoom and Chime are supported), or audio only from any other browser-based meeting app, softphone, or audio source. You can also create your own custom prompts and corresponding options.

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Metadata LLM Automation Large Language Models 134

AWS Machine Learning Blog

JUNE 25, 2024

When the automated content processing steps are complete, you can use the output for downstream tasks, such as to invoke different components in a customer service backend application, or to insert the generated tags into metadata of each document for product recommendation. The LLM generates output based on the user prompt.

Automation 124

Automation Prompt Engineering Prompt Engineer Categorization 124

AWS Machine Learning Blog

MARCH 12, 2024

In this post, we discuss how Leidos worked with AWS to develop an approach to privacy-preserving large language model (LLM) inference using AWS Nitro Enclaves. LLMs are designed to understand and generate human-like language, and are used in many industries, including government, healthcare, financial, and intellectual property.

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Large Language Models LLM Chatbots Natural Language Processing 117

AssemblyAI

DECEMBER 18, 2023

Large Language Models (LLMs) , another component of Speech AI, are powerful AI models that have a robust understanding of general-purpose language and communication. They are made even more accessible through LLM frameworks like LeMUR , which allow companies to easily build Generative AI audio analysis tools on top of spoken data.

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AI AI Artificial Intelligence Artificial Intelligence 147

AWS Machine Learning Blog

JANUARY 10, 2024

The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. Model developers often work together in developing ML models and require a robust MLOps platform to work in. ML Engineer at Tiger Analytics.

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ML Machine Learning Data Scientist ETL 126

AWS Machine Learning Blog

AUGUST 26, 2024

It’s built on diverse data sources and a robust infrastructure layer for data retrieval, prompting, and LLM management. This information is used to build a prompt with relevant context and then fed into an LLM, which generates a response. The final output is a response tailored to the input data and refined through iteration.

Generative AI 125

Generative AI LLM AI AI 125

AWS Machine Learning Blog

AUGUST 8, 2024

Next, you need to index this data to make it available for a Retrieval Augmented Generation (RAG) approach where relevant passages are delivered with high accuracy to a large language model (LLM). Amazon Q supports the crawling and indexing of these custom objects and custom metadata.

Metadata 115

Metadata Generative AI ML IDP 115

ODSC - Open Data Science

JULY 26, 2024

Gen AI in Software Development. Mabel Geronimo | Senior Solutions Engineer | GitHub Dive into the exciting world of AI-assisted development tools to explore the valuable lessons learned and common use cases observed by customers around the globe. What should you be looking for?

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Data Scientist Robotics Metadata Data Science 52

AWS Machine Learning Blog

JUNE 8, 2023

We have included a sample project to quickly deploy an Amazon Lex bot that consumes a pre-trained open-source LLM. This mechanism allows an LLM to recall previous interactions to keep the conversation’s context and pace. We also use LangChain, a popular framework that simplifies LLM-powered applications.

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Mlearning.ai

JULY 7, 2023

That being said, I humbly acknowledge that creating a software application is a very complex process and me not being a software developer have only explored this at a surface level. All nuances of software development remain unexplored. Storing the data and metadata correctly is critical.

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AWS Machine Learning Blog

AUGUST 14, 2024

Next, you need to index this data to make it available for a Retrieval Augmented Generation (RAG) approach, where relevant passages are delivered with high accuracy to a large language model (LLM). Lakshmi Dogiparti is a is a Software Development Engineer at Amazon Web Services.

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Generative AI AI AI Metadata 117

deepsense.ai

SEPTEMBER 11, 2023

There is an increased demand in the job market for developers who are capable of adopting AI tools and willing to do so, with employers valuing the higher efficiency and enriched skill set that these individuals bring to the table. Instead of retreating to a corner, take the proactive route to understanding how these developing tools work.

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AI Developer AI Development Generative AI Software Engineer 105

AWS Machine Learning Blog

JULY 25, 2024

This enables the Amazon Q large language model (LLM) to provide accurate, well-written answers by drawing from the consolidated data and information. Neelam Rana is a Software Development Engineer on the Amazon Q and Amazon Kendra engineering team. Outside of work, he enjoys running, playing tennis, and cooking.

IDP 107

IDP Metadata Generative AI Software Development 107

AWS Machine Learning Blog

AUGUST 27, 2024

These applications can generate answers based on your data or a large language model (LLM) knowledge. Each document has its own attributes, also known as metadata. Metadata can be mapped to fields in your Amazon Q Business index. The following table lists webpage metadata indexed by the Amazon Q Web Crawler connector.

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Metadata Generative AI Large Language Models Software Development 86

Bugra Akyildiz

JUNE 11, 2023

Google built a new system called DIDACT (​​Dynamic Integrated Developer ACTivity), that trains a LLM for all of the software development activities.

Algorithm 52

Algorithm Deep Learning Python NLP 52

O'Reilly Media

JULY 25, 2023

Our paper reinforces the growing consensus that LLM-based AI tools such as ChatGPT and GitHub Copilot can now solve many of the small self-contained programming problems that are found in introductory classes. In sum, this is me trying to simulate the experience of relying as much as possible on ChatGPT to get this project done.

ChatGPT 98

ChatGPT UX Design AI Tools Metadata 98

Bugra Akyildiz

AUGUST 10, 2024

The resulting learned embeddings and associated metadata as features is then inputted to a survival model for predicting 10-year incidence of major adverse cardiac events. vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM is a fast and easy-to-use library for LLM inference and serving.

BERT 59

BERT Large Language Models Categorization Deep Learning 59

Flipboard

DECEMBER 3, 2024

This solution is also deployed by using the AWS Cloud Development Kit (AWS CDK), which is an open-source software development framework that defines cloud infrastructure in modern programming languages and provisions it through AWS CloudFormation.

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AWS Machine Learning Blog

APRIL 19, 2024

of the SageMaker ACK Operators adds support for inference components , which until now were only available through the SageMaker API and the AWS Software Development Kits (SDKs). These controllers allow Kubernetes users to provision AWS resources like buckets, databases, or message queues simply by using the Kubernetes API.

Metadata 124

Metadata LLM Software Development Machine Learning 124

Unite.AI

AUGUST 8, 2024

While single models are suitable in some scenarios, acting as co-pilots, agentic architectures open the door for LLMs to become active components of business process automation. As such, enterprises should consider leveraging LLM-based multi-agent (LLM-MA) systems to streamline complex business processes and improve ROI.

AI Modeling 162

AI Modeling LLM Automation Responsible AI 162

Flipboard

NOVEMBER 24, 2023

This use case highlights how large language models (LLMs) are able to become a translator between human languages (English, Spanish, Arabic, and more) and machine interpretable languages (Python, Java, Scala, SQL, and so on) along with sophisticated internal reasoning.

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ETL Prompt Engineering Prompt Engineer Generative AI 156

NVIDIA

NOVEMBER 17, 2023

Sovereign Cloud, Generative AI Complying with regulations governing how data and metadata can be stored in cloud computing is critical. As a regional CSP, Scaleway also provides sovereign infrastructure that ensures access and compliance with EU data protection laws — critical to businesses with a European footprint.

Metadata 125

Metadata Large Language Models Generative AI Software Development 125

AWS Machine Learning Blog

AUGUST 8, 2023

For example, input images for an object detection use case might need to be resized or cropped before being served to a computer vision model, or tokenization of text inputs before being used in an LLM. The TargetModel parameter is what differentiates MMEs from single-model endpoints and enables us to direct the request to the right model.

BERT 95

BERT Deep Learning Auto-classification Python 95

AWS Machine Learning Blog

DECEMBER 19, 2024

In this blog post, we provide an introduction to preparing your own dataset for LLM training. Whether your goal is to fine-tune a pre-trained modIn this blog post, we provide an introduction to preparing your own dataset for LLM training. nAnswer: He is a software developer, investor, and entrepreneur.

LLM 97

LLM Machine Learning Natural Language Processing ML 97

AWS Machine Learning Blog

JANUARY 15, 2025

Requested information is intelligently fetched from multiple sources such as company product metadata, sales transactions, OEM reports, and more to generate meaningful responses. Vehicle specification data Amazon DynamoDB is used to store the vehicle metadata (its features and specifications).

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LLM Metadata Generative AI Large Language Models 93

AWS Machine Learning Blog

DECEMBER 4, 2024

As enterprises adopt generative AI, many are developing intelligent assistants powered by Retrieval Augmented Generation (RAG) to take advantage of information and knowledge from their enterprise data repositories. This approach combines a retriever with an LLM to generate responses.

Metadata 101

Metadata Generative AI Data Ingestion Software Engineer 101