IBM Journey to AI blog

JANUARY 9, 2024

The entire generative AI pipeline hinges on the data pipelines that empower it, making it imperative to take the correct precautions. 4 key components to ensure reliable data ingestion Data quality and governance: Data quality means ensuring the security of data sources, maintaining holistic data and providing clear metadata.

Data Ingestion 222

Data Ingestion Data Integration Data Quality LLM 222

AWS Machine Learning Blog

MARCH 18, 2025

In the rapidly evolving healthcare landscape, patients often find themselves navigating a maze of complex medical information, seeking answers to their questions and concerns. However, accessing accurate and comprehensible information can be a daunting task, leading to confusion and frustration.

LLM 102

LLM NLP Data Integration AI 102

Unite.AI

SEPTEMBER 18, 2023

To prevent these scenarios, protection of data, user assets, and identity information has been a major focus of the blockchain security research community, as to ensure the development of the blockchain technology, it is essential to maintain its security.

Deep Learning 321

Deep Learning Artificial Intelligence Artificial Intelligence AI 321

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.) But the implementation of AI is only one piece of the puzzle.

Data Scientist 234

Data Scientist Metadata Responsible AI Explainability 234

Unite.AI

DECEMBER 5, 2023

In BI systems, data warehousing first converts disparate raw data into clean, organized, and integrated data, which is then used to extract actionable insights to facilitate analysis, reporting, and data-informed decision-making. Data Sources: Data sources provide information and context to a data warehouse.

Metadata 162

Metadata Big Data ETL Data Mining 162

IBM Journey to AI blog

DECEMBER 16, 2022

So, instead of wandering the aisles in hopes you’ll stumble across the book, you can walk straight to it and get the information you want much faster. An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more.

Metadata 130

Metadata Data Quality Data Discovery Data Scientist 130

Marktechpost

JUNE 5, 2024

Data profiling is a crucial tool. For evaluating data quality. It entails analyzing, cleansing, transforming, and modeling data to find valuable information, improve data quality, and assist in better decision-making, What is Data Profiling? Fixing poor data quality might otherwise cost a lot of money.

Data Quality 113

Data Quality Metadata Data Integration ETL 113

AWS Machine Learning Blog

MARCH 10, 2025

Investment professionals face the mounting challenge of processing vast amounts of data to make timely, informed decisions. This challenge is particularly acute in credit markets, where the complexity of information and the need for quick, accurate insights directly impacts investment outcomes.

DevOps 91

DevOps Metadata Auto-complete Automation 91

IBM Journey to AI blog

JUNE 28, 2024

Based on our experience from proof-of-concept (PoC) projects with clients, here are the best ways to leverage generative AI in the data layer: Understanding vendor data : Generative AI can process extensive vendor documentation to extract critical information about individual parameters.

Generative AI 216

Generative AI Automation Large Language Models AI 216

Marktechpost

MAY 9, 2024

The main features of the platform which are meant to make data workflows more efficient are as follows. Document Extraction: Unstructured is excellent at extracting metadata and document elements from a wide range of document types.

NLP 122

NLP Natural Language Processing Metadata Large Language Models 122

Unite.AI

JUNE 13, 2024

This enhances transparency and reliability, enabling businesses to make informed decisions with confidence. Our platform tracks data lineage, offering full traceability of data and transformations. AI-generated answers are connected back to their data sources, providing a clear trace of how each piece of information was derived.

Explainability 130

Explainability Data Integration Metadata Generative AI 130

IBM Journey to AI blog

AUGUST 4, 2023

Architecture for data democratization Data democratization requires a move away from traditional “data at rest” architecture, which is meant for storing static data. Traditionally, data was seen as information to be put on reserve, only called upon during customer interactions or executing a program.

Machine Learning 183

Machine Learning Metadata Automation AI 183

Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Also Read: Top 10 Data Science tools for 2024.

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ETL Data Integration Data Quality Metadata 40

The MLOps Blog

JUNE 27, 2023

When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. Can you compare images?

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Marktechpost

JULY 7, 2024

Age and Gender Targeting: Ads are delivered based on demographic information such as age and gender, which is collected during user registration or inferred from user behavior. Key components of this model include: User Tower: Captures and encodes user features such as demographic information and browsing history.

Algorithm 106

Algorithm Neural Network Metadata Large Language Models 106

IBM Journey to AI blog

JUNE 17, 2024

Some of the common challenges that enterprises face when protecting data are: Maintaining data integrity and privacy amid the threat of potential data breaches and data leaks. Managing IT budgets while dealing with increased cyberthreats and regulatory compliance.

Data Integration 122

Data Integration Metadata AI AI 122

ODSC - Open Data Science

SEPTEMBER 27, 2023

In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across the globe. They excel at managing structured data and supporting ACID (Atomicity, Consistency, Isolation, Durability) transactions.

Big Data 98

Big Data Metadata ETL Data Science 98

AWS Machine Learning Blog

NOVEMBER 15, 2023

In this post, we demonstrate how data aggregated within the AWS CCI Post Call Analytics solution allowed Principal to gain visibility into their contact center interactions, better understand the customer journey, and improve the overall experience between contact channels while also maintaining data integrity and security.

Data Ingestion 118

Data Ingestion Metadata NLP Data Scientist 118

The MLOps Blog

MAY 10, 2023

Cons of the Python Pickle Approach 1 If you unpickle untrusted data, pickling could pose a security threat. Unpickling an object can execute malicious code, so it’s crucial to only unpickle information from reliable sources. Finally, you can store the model and other metadata information using the INSERT INTO command.

Python 105

Python Metadata ML Machine Learning 105

AWS Machine Learning Blog

SEPTEMBER 28, 2023

Enterprises today face major challenges when it comes to using their information and knowledge bases for both internal and external business operations. Internally, employees can often spend countless hours hunting down information they need to do their jobs, leading to frustration and reduced productivity.

Generative AI 115

Generative AI Artificial Intelligence Artificial Intelligence Large Language Models 115

AWS Machine Learning Blog

OCTOBER 2, 2024

However, scaling up generative AI and making adoption easier for different lines of businesses (LOBs) comes with challenges around making sure data privacy and security, legal, compliance, and operational complexities are governed on an organizational level. For more information, see Monitor Amazon Bedrock with Amazon CloudWatch.

Generative AI 113

Generative AI Data Ingestion AI AI 113

AWS Machine Learning Blog

MAY 15, 2023

Among those algorithms, deep/neural networks are more suitable for e-commerce forecasting problems as they accept item metadata features, forward-looking features for campaign and marketing activities, and – most importantly – related time series features. We are able to forecast over 10,000 SKUs daily in all the countries we serve.

Neural Network 78

Neural Network Convolutional Neural Networks Metadata Data Scientist 78

Pickl AI

APRIL 9, 2025

It provides insights into considerations for choosing the right tool, ensuring businesses can optimize their data integration processes for better analytics and decision-making. Introduction In todays data-driven world, organizations are overwhelmed with vast amounts of information. What is ETL?

ETL 52

ETL Data Integration Business Intelligence Automation 52

IBM Journey to AI blog

JULY 1, 2024

Irina Steenbeek introduces the concept of descriptive lineage as “a method to record metadata-based data lineage manually in a repository.” Extraction, transformation and loading (ETL) tools dominated the data integration scene at the time, used primarily for data warehousing and business intelligence.

ETL 100

ETL Automation Metadata Business Intelligence 100

Pickl AI

JUNE 11, 2024

Summary: A data warehouse is a central information hub that stores and organizes vast amounts of data from different sources within an organization. Unlike operational databases focused on daily tasks, data warehouses are designed for analysis, enabling historical trend exploration and informed decision-making.

ETL 52

ETL Data Mining Data Integration Actionable Intelligence 52

Pickl AI

JULY 19, 2024

They enhance data integrity, security, and accessibility while providing tools for efficient data management and retrieval. It serves as a robust intermediary between end-users, applications, and the underlying database, ensuring data integrity, security, accessibility, and overall efficiency.

Data Integration 52

Data Integration Metadata Big Data Business Intelligence 52

Pickl AI

AUGUST 31, 2023

Introduction The presence of large volumes of data within organisations requires effective sorting and analysing ensuring that decision-making is highly credible. Almost all organisations nowadays make informed decisions by leveraging data and analysing the market effectively. What is Data Profiling in ETL?

ETL 52

ETL Data Quality Data Integration Metadata 52

IBM Journey to AI blog

SEPTEMBER 17, 2024

Its in-memory processing helps to ensure that data is ready for quick analysis and reporting, enabling real-time what-if scenarios and reports without lag. Our solution handles massive multidimensional cubes seamlessly, enabling you to maintain a complete view of your data without sacrificing performance or data integrity.

Big Data 100

Big Data Metadata Business Intelligence Data Integration 100

Pickl AI

OCTOBER 11, 2024

These include the database engine for executing queries, the query processor for interpreting SQL commands, the storage manager for handling physical data storage, and the transaction manager for ensuring data integrity through ACID properties. Data Independence: Changes in database structure do not affect application programs.

Data Integration 52

Data Integration Metadata Big Data Algorithm 52

AWS Machine Learning Blog

AUGUST 7, 2024

Challenge As AI becomes ubiquitous, it’s increasingly used to process information and interact with customers in a sensitive context. For cross-Region copying, see Copy data from an S3 bucket to another account and Region by using the AWS CLI. Suppose a tax agency is interacting with its users through a chatbot.

Automation 112

Automation Machine Learning ML Explainability 112

Pickl AI

OCTOBER 21, 2024

The primary purpose of a DBMS is to provide a systematic way to manage large amounts of data, ensuring that it is organised, accessible, and secure. By employing a DBMS, organisations can maintain data integrity, reduce redundancy, and streamline data operations, enabling more informed decision-making.

Data Integration 52

Data Integration Metadata Machine Learning Algorithm 52

Pickl AI

JANUARY 27, 2025

This blog aims to clarify Big Data concepts, illuminate Hadoops role in modern data handling, and further highlight how HDFS strengthens scalability, ensuring efficient analytics and driving informed business decisions. Key Takeaways HDFS in Big Data distributes large files across commodity servers, reducing hardware costs.

Big Data 52

Big Data Data Integration ETL Metadata 52

ODSC - Open Data Science

JUNE 7, 2023

Data lakes are able to handle a diverse range of data types. From images, videos, text, and even sensor data. Then, there’s data integration. A data lake can also act as a central hub for integrating data from various sources and systems within an organization.

Metadata 52

Metadata Data Science Machine Learning Python 52

Pickl AI

JUNE 28, 2023

Data is driving most business decisions. In this, data modeling tools play a crucial role in developing and maintaining the information system. Moreover, it involves the creation of a conceptual representation of data and its relationship. Data modeling tools play a significant role in this.

Metadata 52

Metadata Data Integration Automation Software Development 52

Pickl AI

NOVEMBER 15, 2023

This flexibility allows organizations to store vast amounts of raw data without the need for extensive preprocessing, providing a comprehensive view of information. Centralized Data Repository Data Lakes serve as a centralized repository, consolidating data from different sources within an organization.

ETL 52

ETL Metadata Business Intelligence Data Analysis 52

Pickl AI

APRIL 14, 2023

Data Processes and Organizational Structure Data Governance access controls enable the end-users to see how data processing works inside an organization. It can include data refresh cadences, PII limitations, regulatory data regulations, or even data access. It ensures the safe storage of data.

Data Platform 52

Data Platform Data Integration Data Ingestion Automation 52

Flipboard

NOVEMBER 15, 2024

Metadata can play a very important role in using data assets to make data driven decisions. Generating metadata for your data assets is often a time-consuming and manual task. First, we explore the option of in-context learning, where the LLM generates the requested metadata without documentation.

Metadata 148

Metadata Generative AI LLM AI 148