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.

Metadata 162

Metadata Big Data ETL Data Mining 162

IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases.

Data Quality 130

Data Quality Metadata ETL Big Data 130

AWS Machine Learning Blog

AUGUST 8, 2024

This post highlights how Twilio enabled natural language-driven data exploration of business intelligence (BI) data with RAG and Amazon Bedrock. Twilio’s use case Twilio wanted to provide an AI assistant to help their data analysts find data in their data lake.

Metadata 130

Metadata LLM Prompt Engineer Prompt Engineering 130

AWS Machine Learning Blog

NOVEMBER 15, 2023

PCA provides an entire architecture around ingesting audio files in a fully automated workflow with AWS Step Functions , which is initiated when an audio file is delivered to a configured Amazon Simple Storage Service (Amazon S3) bucket. PCA also offers a web-based user interface that allows customers to browse call transcripts.

Data Ingestion 128

Data Ingestion Metadata NLP Data Scientist 128

AWS Machine Learning Blog

OCTOBER 2, 2024

Additionally, you can enable model invocation logging to collect invocation logs, full request response data, and metadata for all Amazon Bedrock model API invocations in your AWS account. Before you can enable invocation logging, you need to set up an Amazon Simple Storage Service (Amazon S3) or CloudWatch Logs destination.

Generative AI 131

Generative AI Data Ingestion AI AI 131

Mlearning.ai

FEBRUARY 16, 2023

Today, companies are facing a continual need to store tremendous volumes of data. The demand for information repositories enabling business intelligence and analytics is growing exponentially, giving birth to cloud solutions. Therefore, quick data ingestion for instant use can be challenging.

Business Intelligence 52

Business Intelligence Data Ingestion Metadata Machine Learning 52

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.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69