DataKitchen

FEBRUARY 27, 2024

Your LLM Needs a Data Journey: A Comprehensive Guide for Data Engineers The rise of Large Language Models (LLMs) such as GPT-4 marks a transformative era in artificial intelligence, heralding new possibilities and challenges in equal measure.

Data Quality 117

Data Quality Unstructured Data Testing Modeling 117

IBM Big Data Hub

NOVEMBER 29, 2023

Non-symbolic AI can be useful for transforming unstructured data into organized, meaningful information. This helps to simplify data analysis and enable informed decision-making. Unstructured data interpretation: Unstructured data can often contain untapped insights.

Unstructured Data 85

Unstructured Data Data-driven ROI Machine Learning 85

Smart Data Collective

SEPTEMBER 26, 2019

For example, you can organize an employee table in a database in a structured manner to capture the employee’s details, job positions, salary, etc. Unstructured. Unstructured data lacks a specific format or structure. As a result, processing and analyzing unstructured data is super-difficult and time-consuming.

Big Data 80

Big Data Software Unstructured Data Data Integration 80

AWS Big Data

MAY 28, 2024

Large language models (LLMs) such as Anthropic Claude and Amazon Titan have the potential to drive automation across various business processes by processing both structured and unstructured data. For getting data from Amazon Redshift, we use the Anthropic Claude 2.0 This is unstructured data augmentation to the LLM.

Unstructured Data 89

Unstructured Data Structured Data Data Warehouse Testing 89

IBM Big Data Hub

JULY 6, 2023

The development of generative AI —which uses powerful foundation models that train on large amounts of unlabeled data—can be adapted to new use cases and bring flexibility and scalability that is likely to accelerate the adoption of AI significantly. It can ingest unstructured data in its raw form (e.g.,

Deep Learning 57

Deep Learning Machine Learning Unstructured Data Modeling 57

Cloudera

JULY 26, 2021

Authorization: Define what users of internal / external organizations can access and do with the data in a fine-grained manner that ensures compliance with e.g., data obfuscation requirements introduced by industry and country specific standards for certain types of data assets such as PII.

Strategy 119

Strategy Data Science Marketing Unstructured Data 119

Cloudera

OCTOBER 7, 2021

Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide data lakes versus smaller, typically BU-Specific, “data ponds”.

Metadata 127

Metadata Cost-Benefit Enterprise Interactive 127