IBM Journey to AI blog

OCTOBER 12, 2023

Among the tasks necessary for internal and external compliance is the ability to report on the metadata of an AI model. Metadata includes details specific to an AI model such as: The AI model’s creation (when it was created, who created it, etc.)

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Marktechpost

JUNE 5, 2024

Analytics, management, and business intelligence (BI) procedures, such as data cleansing, transformation, and decision-making, rely on data profiling. Content and quality reviews are becoming more important as data sets grow in size and variety of sources. Data profiling is a crucial tool.

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IBM Journey to AI blog

OCTOBER 14, 2024

“ Gen AI has elevated the importance of unstructured data, namely documents, for RAG as well as LLM fine-tuning and traditional analytics for machine learning, business intelligence and data engineering,” says Edward Calvesbert, Vice President of Product Management at IBM watsonx and one of IBM’s resident data experts.

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Unite.AI

DECEMBER 5, 2023

This article will explore data warehousing, its architecture types, key components, benefits, and challenges. What is Data Warehousing? Data warehousing is a data management system to support Business Intelligence (BI) operations. It can handle vast amounts of data and facilitate complex queries.

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IBM Journey to AI blog

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

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ODSC - Open Data Science

SEPTEMBER 27, 2023

Storage Optimization: Data warehouses use columnar storage formats and indexing to enhance query performance and data compression. Here are some essential strategies: Time-Stamped Snapshots: Maintaining time-stamped snapshots of the data allows for a historical view of changes made over time.

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AWS Machine Learning Blog

MARCH 18, 2025

Regulatory compliance By integrating the extracted insights and recommendations into clinical trial management systems and EHRs, this approach facilitates compliance with regulatory requirements for data capture, adverse event reporting, and trial monitoring. Solution overview The following diagram illustrates the solution architecture.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

Therefore, when the Principal team started tackling this project, they knew that ensuring the highest standard of data security such as regulatory compliance, data privacy, and data quality would be a non-negotiable, key requirement.

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Pickl AI

NOVEMBER 15, 2023

On the other hand, a Data Warehouse is a structured storage system designed for efficient querying and analysis. It involves the extraction, transformation, and loading (ETL) process to organize data for business intelligence purposes. It often serves as a source for Data Warehouses. What Is Data Lake Architecture?

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Pickl AI

DECEMBER 2, 2024

This includes structured data (like databases), semi-structured data (like XML files), and unstructured data (like text documents and videos). For instance, Netflix uses diverse data types—from user viewing habits to movie metadata—to provide personalised recommendations. How Does Big Data Ensure Data Quality?

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Pickl AI

NOVEMBER 25, 2024

This includes structured data (like databases), semi-structured data (like XML files), and unstructured data (like text documents and videos). For instance, Netflix uses diverse data types—from user viewing habits to movie metadata—to provide personalised recommendations. How Does Big Data Ensure Data Quality?

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Pickl AI

JULY 29, 2024

Master Nodes The master node is responsible for managing the cluster’s resources and coordinating the data processing tasks. It typically runs several critical services: NameNode: This service manages the Hadoop Distributed File System (HDFS) metadata, keeping track of the location of data blocks across the cluster.

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ODSC - Open Data Science

DECEMBER 3, 2024

The open-source data catalogs provide several key features that are beneficial for a data mesh. These include a centralized metadata repository to enable the discovery of data assets across decentralized data domains. Maintain the data mesh infrastructure. Train the teams.

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Pickl AI

JANUARY 16, 2024

billion 28% AI-Powered Data Analytics Transformation in decision-making speed. billion 15.83% Metadata-Driven Data Fabric Systematic data management efficiency. Value in 2022 – $18.10 billion Value by 2030 – $125.64 Value in 2024 – $305.90 Billion Value by 20230 – $738.80 Value in 2021 – $1.12

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AWS Machine Learning Blog

AUGUST 12, 2024

AWS data engineering pipeline The adaptable approach detailed in this post starts with an automated data engineering pipeline to make data stored in Splunk available to a wide range of personas, including business intelligence (BI) analysts, data scientists, and ML practitioners, through a SQL interface.

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The MLOps Blog

MARCH 21, 2023

To make that possible, your data scientists would need to store enough details about the environment the model was created in and the related metadata so that the model could be recreated with the same or similar outcomes. Your ML platform must have versioning in-built because code and data mostly make up the ML system.

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IBM Journey to AI blog

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata.

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Pickl AI

DECEMBER 15, 2024

Explore popular data warehousing tools and their features. Emphasise the importance of data quality and security measures. Data Warehouse Interview Questions and Answers Explore essential data warehouse interview questions and answers to enhance your preparation for 2025. What Is Metadata in Data Warehousing?

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Pickl AI

FEBRUARY 9, 2025

As a software suite, it encompasses a range of interconnected products, including Tableau Desktop, Server, Cloud, Public, Prep, and Data Management, and Reader. At its core, it is designed to help people see and understand data. It disrupts traditional business intelligence with intuitive, visual analytics for everyone.

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