Unite.AI

DECEMBER 5, 2023

ETL ( Extract, Transform, Load ) Pipeline: It is a data integration mechanism responsible for extracting data from data sources, transforming it into a suitable format, and loading it into the data destination like a data warehouse. The pipeline ensures correct, complete, and consistent data.

Metadata 162

Metadata Big Data ETL Data Mining 162

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 126

Metadata LLM Prompt Engineering Prompt Engineer 126

AWS Machine Learning Blog

APRIL 5, 2023

The dataset is a collection of 147,702 product listings with multilingual metadata and 398,212 unique catalogue images. There are 16 files that include product description and metadata of Amazon products in the format of listings/metadata/listings_.json.gz. We use the first metadata file in this demo.

Metadata 110

Metadata Neural Network ML Python 110

Pickl AI

JULY 6, 2023

These work together to enable efficient data processing and analysis: · Hive Metastore It is a central repository that stores metadata about Hive’s tables, partitions, and schemas. It applies the data structure during querying rather than data ingestion. Why Do We Need Hadoop Hive?

Big Data 52

Big Data Data Analysis ETL Metadata 52

deepsense.ai

JULY 30, 2023

Preceded by data analysis and feature engineering, a model is trained and ready to be productionized. We may observe a growing awareness among machine learning and data science practitioners of the crucial role played by pre- and post-training activities. But what happens next? This triggers a bunch of quality checks (e.g.

Large Language Models 98

Large Language Models LLM Machine Learning Automation 98

Pickl AI

APRIL 14, 2023

In addition, it also defines the framework wherein it is decided what action needs to be taken on certain data. And so, a company dealing in Big Data Analysis needs to follow stringent Data Governance policies. It can include data refresh cadences, PII limitations, regulatory data regulations, or even data access.

Data Platform 52

Data Platform Data Integration Data Ingestion Automation 52

The MLOps Blog

MAY 9, 2023

The components comprise implementations of the manual workflow process you engage in for automatable steps, including: Data ingestion (extraction and versioning). Data validation (writing tests to check for data quality). Data preprocessing. Let’s briefly go over each of the components below. CSV, Parquet, etc.)

ML 98

ML Machine Learning Metadata Data Science 98