AWS Machine Learning Blog

MARCH 1, 2023

Solution overview Amazon Comprehend is a fully managed service that uses natural language processing (NLP) to extract insights about the content of documents. This feature also allows you to automate model retraining after new datasets are ingested and available in the flywheel´s data lake.

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Flipboard

NOVEMBER 17, 2023

Generative AI models have the potential to revolutionize enterprise operations, but businesses must carefully consider how to harness their power while overcoming challenges such as safeguarding data and ensuring the quality of AI-generated content. As a Data Engineer he was involved in applying AI/ML to fraud detection and office automation.

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AWS Machine Learning Blog

SEPTEMBER 27, 2024

This archive, along with 765,933 varied-quality inspection photographs, some over 15 years old, presented a significant data processing challenge. Processing these images and scanned documents is not a cost- or time-efficient task for humans, and requires highly performant infrastructure that can reduce the time to value.

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AWS Machine Learning Blog

MAY 30, 2024

To serve their customers, Vitech maintains a repository of information that includes product documentation (user guides, standard operating procedures, runbooks), which is currently scattered across multiple internal platforms (for example, Confluence sites and SharePoint folders).

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AWS Machine Learning Blog

APRIL 3, 2025

Amazon AppFlow was used to facilitate the smooth and secure transfer of data from various sources into ODAP. Additionally, Amazon Simple Storage Service (Amazon S3) served as the central data lake, providing a scalable and cost-effective storage solution for the diverse data types collected from different systems.

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AWS Machine Learning Blog

AUGUST 2, 2024

The Product Stewardship department is responsible for managing a large collection of regulatory compliance documents. Example questions might be “What are the restrictions for CMR substances?”, “How long do I need to keep the documents related to a toluene sale?”, or “What is the reach characterization ratio and how do I calculate it?”

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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. Poor data integration can lead to inaccurate insights.

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DagsHub

DECEMBER 11, 2023

Lake File System ( LakeFS for short) is an open-source version control tool, launched in 2020, to bridge the gap between version control and those big data solutions (data lakes). It provides ACID transactions, scalable metadata management, and schema enforcement to data lakes.

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Flipboard

MARCH 7, 2023

Then the transcripts of contacts become available to CSBA to extract actionable insights through millions of customer contacts for the sellers, and the data is stored in the Seller Data Lake. After the AI/ML-based analytics, all actionable insights are generated and then stored in the Seller Data Lake.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

The IDP Well-Architected Lens is intended for all AWS customers who use AWS to run intelligent document processing (IDP) solutions and are searching for guidance on how to build secure, efficient, and reliable IDP solutions on AWS. This post focuses on the Operational Excellence pillar of the IDP solution.

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AWS Machine Learning Blog

NOVEMBER 6, 2024

You can find instructions on how to do this in the AWS documentation for your chosen SDK. AWS credentials – Configure your AWS credentials in your development environment to authenticate with AWS services. We walk through a Python example in this post.

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AWS Machine Learning Blog

JANUARY 29, 2025

Intelligent document processing , translation and summarization, flexible and insightful responses for customer support agents, personalized marketing content, and image and code generation are a few use cases using generative AI that organizations are rolling out in production.

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Flipboard

OCTOBER 25, 2023

Text, images, audio, and videos are common examples of unstructured data. Most companies produce and consume unstructured data such as documents, emails, web pages, engagement center phone calls, and social media. The steps of the workflow are as follows: Integrated AI services extract data from the unstructured data.

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Data Science 101

DECEMBER 16, 2019

It now also supports PDF documents. Azure Data Factory Preserves Metadata during File Copy When performing a File copy between Amazon S3, Azure Blob, and Azure Data Lake Gen 2, the metadata will be copied as well. Not a huge update but still a nice feature. Azure Database for MySQL now supports MySQL 8.0

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How to Learn Machine Learning

MAY 2, 2025

You can explore its capabilities through the official Azure ML Studio documentation. Azure ML SDK : For those who prefer a code-first approach, the Azure Machine Learning Python SDK allows data scientists to work in familiar environments like Jupyter notebooks while leveraging Azure’s capabilities.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

The agent knowledge base stores Amazon Bedrock service documentation, while the cache knowledge base contains curated and verified question-answer pairs. For this example, you will ingest Amazon Bedrock documentation in the form of the User Guide PDF into the Amazon Bedrock knowledge base. This will be the primary dataset.

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IBM Journey to AI blog

APRIL 15, 2024

Or we create a data lake, which quickly degenerates to a data swamp. Various initiatives to create a knowledge graph of these systems have been only partially successful due to the depth of legacy knowledge, incomplete documentation and technical debt incurred over decades.

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Alation

FEBRUARY 1, 2022

Menninger states that modern data governance programs can provide a more significant ROI at a much faster pace. Ventana found that the most time-consuming part of an organization’s analytic efforts is accessing and preparing data; this is the case for more than one-half (55%) of respondents. Curious to learn more?

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Towards AI

FEBRUARY 13, 2023

How to build a chatbot that answers questions about documentation and cites its sources The tutorial was initially hosted via a live stream on our Learn AI Discord. Three 5-minute reads/videos to keep you learning 1.How These considerations include cost, complexity, expertise, time to value, and competitive advantage.

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Data Science Dojo

SEPTEMBER 10, 2024

It also excels at creating concise, relevant, and customizable summaries of text and documents. Embedding Models : Cohere’s embedding models enhance applications by understanding the meaning of text data at scale. Trained to respond to user instructions, Command proves immediately valuable in practical business applications.

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AWS Machine Learning Blog

JUNE 20, 2024

Our goal was to improve the user experience of an existing application used to explore the counters and insights data. The data is stored in a data lake and retrieved by SQL using Amazon Athena. The question is sent through a retrieval-augmented generation (RAG) process, which finds similar documents.

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ODSC - Open Data Science

FEBRUARY 24, 2023

Great Expectations GitHub | Website Great Expectations (GX) helps data teams build a shared understanding of their data through quality testing, documentation, and profiling. With Great Expectations , data teams can express what they “expect” from their data using simple assertions.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

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DagsHub

OCTOBER 23, 2024

To combine the collected data, you can integrate different data producers into a data lake as a repository. A central repository for unstructured data is beneficial for tasks like analytics and data virtualization. Data Cleaning The next step is to clean the data after ingesting it into the data lake.

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IBM Journey to AI blog

AUGUST 1, 2023

models are trained on IBM’s curated, enterprise-focused data lake. Fortunately, data stores serve as secure data repositories and enable foundation models to scale in both terms of their size and their training data. Foundation models focused on enterprise value IBM’s watsonx.ai All watsonx.ai

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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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Alation

AUGUST 4, 2022

And where data was available, the ability to access and interpret it proved problematic. Big data can grow too big fast. Left unchecked, data lakes became data swamps. Some data lake implementations required expensive ‘cleansing pumps’ to make them navigable again.

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The MLOps Blog

JUNE 27, 2023

User support arrangements Consider the availability and quality of support from the provider or vendor, including documentation, tutorials, forums, customer service, etc. Check out the Kubeflow documentation. Metaflow Metaflow helps data scientists and machine learning engineers build, manage, and deploy data science projects.

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AWS Machine Learning Blog

FEBRUARY 28, 2024

Third, despite the larger adoption of centralized analytics solutions like data lakes and warehouses, complexity rises with different table names and other metadata that is required to create the SQL for the desired sources. You may have to recreate the capability for every database to enable users with NLP-based SQL generation.

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IBM Journey to AI blog

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools.

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Snorkel AI

JULY 12, 2024

Statistical Data Analysis: Oftentimes, important information is buried within a document that contains important clues for labeling. If these documents need to be manually processed to pull out information needed for model training, then that would be an arduous and error-prone process.

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Snorkel AI

JULY 12, 2024

Statistical Data Analysis: Oftentimes, important information is buried within a document that contains important clues for labeling. If these documents need to be manually processed to pull out information needed for model training, then that would be an arduous and error-prone process.

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Alation

FEBRUARY 13, 2020

Figure 1 illustrates the typical metadata subjects contained in a data catalog. Figure 1 – Data Catalog Metadata Subjects. Datasets are the files and tables that data workers need to find and access. They may reside in a data lake, warehouse, master data repository, or any other shared data resource.

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AWS Machine Learning Blog

AUGUST 2, 2023

Amazon Kendra supports a variety of document formats , such as Microsoft Word, PDF, and text from various data sources. In this post, we focus on extending the document support in Amazon Kendra to make images searchable by their displayed content. This means you can manipulate and ingest your data as needed.

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Pickl AI

APRIL 21, 2025

Semi-Structured Data: Data that has some organizational properties but doesn’t fit a rigid database structure (like emails, XML files, or JSON data used by websites). Unstructured Data: Data with no predefined format (like text documents, social media posts, images, audio files, videos).

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AWS Machine Learning Blog

MAY 31, 2024

Challenges and considerations with RAG architectures Typical RAG architecture at a high level involves three stages: Source data pre-processing Generating embeddings using an embedding LLM Storing the embeddings in a vector store. Vector embeddings include the numeric representations of text data within your documents.

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