AWS Machine Learning Blog

JANUARY 29, 2025

RAG is an advanced natural language processing technique that combines knowledge retrieval with generative text models. RAG combines the powers of pre-trained language models with a retrieval-based approach to generate more informed and accurate responses.

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AWS Machine Learning Blog

JUNE 25, 2024

Businesses can use LLMs to gain valuable insights, streamline processes, and deliver enhanced customer experiences. In the first step, an AWS Lambda function reads and validates the file, and extracts the raw data. The raw data is processed by an LLM using a preconfigured user prompt. The Step Functions workflow starts.

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Dataconomy

SEPTEMBER 4, 2023

Text analytics: Text analytics, also known as text mining, deals with unstructured text data, such as customer reviews, social media comments, or documents. It uses natural language processing (NLP) techniques to extract valuable insights from textual data. Ensure that data is clean, consistent, and up-to-date.

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AWS Machine Learning Blog

JUNE 13, 2023

The combination of large language models (LLMs), including the ease of integration that Amazon Bedrock offers, and a scalable, domain-oriented data infrastructure positions this as an intelligent method of tapping into the abundant information held in various analytics databases and data lakes.

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AWS Machine Learning Blog

JUNE 21, 2024

eSentire has over 2 TB of signal data stored in their Amazon Simple Storage Service (Amazon S3) data lake. This further step updates the FM by training with data labeled by security experts (such as Q&A pairs and investigation conclusions).

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AWS Machine Learning Blog

FEBRUARY 28, 2024

Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. This generative AI task is called text-to-SQL, which generates SQL queries from natural language processing (NLP) and converts text into semantically correct SQL. on Amazon Bedrock.

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Pickl AI

DECEMBER 2, 2024

As organisations grapple with this vast amount of information, understanding the main components of Big Data becomes essential for leveraging its potential effectively. Key Takeaways Big Data originates from diverse sources, including IoT and social media. Data lakes and cloud storage provide scalable solutions for large datasets.

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Pickl AI

NOVEMBER 25, 2024

As organisations grapple with this vast amount of information, understanding the main components of Big Data becomes essential for leveraging its potential effectively. Key Takeaways Big Data originates from diverse sources, including IoT and social media. Data lakes and cloud storage provide scalable solutions for large datasets.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

ML operationalization summary As defined in the post MLOps foundation roadmap for enterprises with Amazon SageMaker , ML and operations (MLOps) is the combination of people, processes, and technology to productionize machine learning (ML) solutions efficiently. In the case of FMs, they need either billions of labeled or unlabeled data points.

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DagsHub

NOVEMBER 11, 2024

Social media conversations, comments, customer reviews, and image data are unstructured in nature and hold valuable insights, many of which are still being uncovered through advanced techniques like Natural Language Processing (NLP) and machine learning. Tools like Unstructured.io

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The MLOps Blog

JUNE 27, 2023

Learn more The Best Tools, Libraries, Frameworks and Methodologies that ML Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP] Key use cases and/or user journeys Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively.

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DagsHub

OCTOBER 23, 2024

There are 5 stages in unstructured data management: Data collection Data integration Data cleaning Data annotation and labeling Data preprocessing Data Collection The first stage in the unstructured data management workflow is data collection. mp4,webm, etc.), and audio files (.wav,mp3,acc,

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IBM Journey to AI blog

JULY 17, 2023

This allows users to accomplish different Natural Language Processing (NLP) functional tasks and take advantage of IBM vetted pre-trained open-source foundation models. Encoder-decoder and decoder-only large language models are available in the Prompt Lab today. To bridge the tuning gap, watsonx.ai

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IBM Journey to AI blog

OCTOBER 16, 2023

Foundation models: The power of curated datasets Foundation models , also known as “transformers,” are modern, large-scale AI models trained on large amounts of raw, unlabeled data. It allows for automation and integrations with existing databases and provides tools that permit a simplified setup and user experience.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

As a first step, they wanted to transcribe voice calls and analyze those interactions to determine primary call drivers, including issues, topics, sentiment, average handle time (AHT) breakdowns, and develop additional natural language processing (NLP)-based analytics. The Results PCA helped accelerate time to market.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Mai-Lan Tomsen Bukovec, Vice President, Technology | AIM250-INT | Putting your data to work with generative AI Thursday November 30 | 12:30 PM – 1:30 PM (PST) | Venetian | Level 5 | Palazzo Ballroom B How can you turn your data lake into a business advantage with generative AI?

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IBM Journey to AI blog

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. After all, Alex may not be aware of all the data available to her.

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ODSC - Open Data Science

OCTOBER 15, 2023

Cloudera Cloudera is a cloud-based platform that provides businesses with the tools they need to manage and analyze data. They offer a variety of services, including data warehousing, data lakes, and machine learning. ArangoDB ArangoDB is a company that provides a database platform for graph and document data.

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IBM Journey to AI blog

OCTOBER 16, 2023

Voice-based queries use Natural Language Processing (NLP) and sentiment analysis for speech recognition. Customer service use cases Not only can ML understand what customers are saying, but it also understands their tone and can direct them to appropriate customer service agents for customer support.

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Dataversity

FEBRUARY 24, 2021

The amount of data generated in the digital world is increasing by the minute! This massive amount of data is termed “big data.” We may classify the data as structured, unstructured, or semi-structured. Data that is structured or semi-structured is relatively easy to store, process, and analyze. […].

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Pickl AI

SEPTEMBER 5, 2024

Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.

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AWS Machine Learning Blog

JANUARY 26, 2024

Also consider using Amazon Security Lake to automatically centralize security data from AWS environments, SaaS providers, on premises, and cloud sources into a purpose-built data lake stored in your account. Emily Soward is a Data Scientist with AWS Professional Services.

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AWS Machine Learning Blog

AUGUST 2, 2024

Each document is divided into chunks to ease the indexing and retrieval processes based on semantic meaning. Embeddings generation – An embeddings model is used to encode the semantic information of each chunk into an embeddings vector, which is stored in a vector database, enabling similarity search of user queries.

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The MLOps Blog

MARCH 21, 2023

The job reads features, generates predictions, and writes them to a database. The client queries and reads the predictions from the database when needed. Inside the engine is a metrics data processor that: Reads the telemetry data, Calculates different operational metrics at regular intervals, And stores them in a metrics database.

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AWS Machine Learning Blog

OCTOBER 11, 2024

This post dives deep into Amazon Bedrock Knowledge Bases , which helps with the storage and retrieval of data in vector databases for RAG-based workflows, with the objective to improve large language model (LLM) responses for inference involving an organization’s datasets. The LLM response is passed back to the agent.

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AWS Machine Learning Blog

FEBRUARY 12, 2025

Many organizations store their data in structured formats within data warehouses and data lakes. Amazon Bedrock Knowledge Bases offers a feature that lets you connect your RAG workflow to structured data stores. The key is to choose a solution that can effectively host your database and compute infrastructure.

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