Alation

FEBRUARY 20, 2020

When it was no longer a hard requirement that a physical data model be created upon the ingestion of data, there was a resulting drop in richness of the description and consistency of the data stored in Hadoop. You did not have to understand or prepare the data to get it into Hadoop, so people rarely did.

Data Lakes 52

Data Lakes Hadoop Tableau Big Data 52

ODSC - Open Data Science

SEPTEMBER 29, 2023

Data management problems can also lead to data silos; disparate collections of databases that don’t communicate with each other, leading to flawed analysis based on incomplete or incorrect datasets. One way to address this is to implement a data lake: a large and complex database of diverse datasets all stored in their original format.

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

AWS Machine Learning Blog

MARCH 1, 2023

Amazon Comprehend is a managed AI service that uses natural language processing (NLP) with ready-made intelligence to extract insights about the content of documents. It develops insights by recognizing the entities, key phrases, language, sentiments, and other common elements in a document.

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Data Lakes AWS ML ML 96

AWS Machine Learning Blog

DECEMBER 3, 2024

Although generative AI is fueling transformative innovations, enterprises may still experience sharply divided data silos when it comes to enterprise knowledge, in particular between unstructured content (such as PDFs, Word documents, and HTML pages), and structured data (real-time data and reports stored in databases or data lakes).

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AWS Database Data Silos Data Lakes 103

Smart Data Collective

AUGUST 17, 2022

You can safely use an Apache Kafka cluster for seamless data movement from the on-premise hardware solution to the data lake using various cloud services like Amazon’s S3 and others. It will enable you to quickly transform and load the data results into Amazon S3 data lakes or JDBC data stores.

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Apache Kafka ETL Data Lakes AWS 130

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.

Data Lakes 98

Data Lakes AWS ML ML 98

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 Data Lakes ML ML 122

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.

K-nearest Neighbors 170

K-nearest Neighbors AWS Clustering Database 170

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 Data Governance Data Silos SQL 86

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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AWS Machine Learning Machine Learning Database 128

AWS Machine Learning Blog

FEBRUARY 12, 2025

Retriever quality For better retrieval performance, the way the data is stored in the vector store has a big impact. For example, your input document might include tables within the PDF. In such cases, using an FM to parse the data will provide better results.

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AWS Machine Learning Machine Learning AI 98

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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AI AI AWS Database 132

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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Analytics Analytics Big Data Analytics Big Data Analytics 203

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.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Data Science 98

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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AWS AI AI Database 99

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 Python Machine Learning Machine Learning 121

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.

ML 123

ML ML AWS AI 123

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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AWS ML ML Analytics 167

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

Cloud Data 81

Cloud Data Data Science Azure Natural Language Processing 81

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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AWS Natural Language Processing Machine Learning Machine Learning 126

Flipboard

FEBRUARY 19, 2025

Robust data processing pipeline A sophisticated data processing pipeline maintains Alfreds knowledge base and promotes high-quality responses. Amazon S3 serves as the central data lake, providing scalable object storage for raw documents with versioning and lifecycle management capabilities.

AWS 81

AWS Cloud Computing Natural Language Processing Data Lakes 81

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.

AI 116

AI AI Data Lakes Analytics 116

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?

Data Lakes 52

Data Lakes Data Governance ML ML 52

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.

Azure 52

Azure Machine Learning Machine Learning Data Science 52

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.

AI 98

AI AI Data Warehouse Data Lakes 98

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.

Machine Learning 369

Machine Learning Machine Learning Natural Language Processing ML 369

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.

SQL 116

SQL Database AWS Machine Learning 116

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.

AI 128

AI AI ML ML 128

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.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

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.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

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

AI 70

AI AI Machine Learning Machine Learning 70

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.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

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.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

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.

Big Data 95

Big Data Big Data Apache Kafka Data Lakes 95

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.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

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.

SQL 142

SQL AWS Database ML 142